Enhancing capacity for next generation sequencing (NGS ......Diagnostic mutation detection using...
Transcript of Enhancing capacity for next generation sequencing (NGS ......Diagnostic mutation detection using...
Enhancing capacity for next generation
sequencing (NGS) and genomics in health,
agricultural, ecological and environmental
applications in Kazakhstan
A Newton-Al-Farabi Partnership Programme Researcher links
workshop
September 20th-23rd 2016
Hotel Grand Voyage, Almaty, Kazakhstan
Organised by School of Biology, Univeristy of Leeds, UK &
Institute of Microbiology & Virology, Almaty, Kazakhstan
Workshop Overview
Next generation sequencing and genomics are technologies that have developed at an
astonishing rate in recent years. They have become fundamental to health research and
diagnostics, including genetic disease and cancer, infectious disease, bacterial drug resistance
and personalised drug treatments for patients. It is also central to many areas of agricultural,
ecological and environmental research and diagnostics. This workshop will bring together UK
researchers using NGS and genomics in different fields, with Kazakh scientists with the aim of
fostering links that will help enhance their capability to apply this new technology in their own
work, to identify new opportunities for collaborative research projects between the UK and
Kazakhstan and to promote career development of young scientists. Overall we hope this will
promote the ability of Kazakh scientists to apply cutting edge NGS/genomics techniques in
health care, agricultural and environmental endeavours and support new biotechnology
enterprises.
https://goodmanlab.org/research/workshops-meetings/next-generation-sequencing-
researcher-links-workshop-almaty-kazakhstan-september-18th-24th-2016/
Workshop #tag: #AlmatyNGS2016
Dr Simon Goodman School of Biology University of Leeds Woodhouse Lane Leeds LS2 9JT UK
Dr Kobey Karamendin Institute of Microbiology and Virology 103 Bogenbai batyr str. Almaty, 050010 Kazakhstan
Programme
Mon 19th September
Registration from 17.00 to 19.00
Tues 20th September
09.00-10.00 Registration
10.00-10.10 Opening remarks
10.10-11.00 Plenary Dr Aynur Akilzhanova (Nazarbayev University) Genomic research in Kazakhstan: Challenges and opportunities for clinical applications
11.00-11.30 Coffee break
11.30-12.20 Plenary Dr Ian Carr (St Jamesβs Hospital, University of Leeds) Diagnostic mutation detection using Next Generation Sequencing for healthcare in the UK
12.20-12.50 Ms Morag Taylor (St Jamesβs Hospital, University of Leeds) The Role of Next Generation Sequencing in colorectal cancer research
12.50-14.00 Lunch
14.00-14.20 Dr Saule Rakhimova (Nazarbayev University) Transcriptome profiling of oesophageal cancer: from sampling to sequencing on HiSeq2000
14.20-14.40 Dr Ulykbek Kairov (Nazarbayev University) Meta-analysis of cancer transcriptome profiles using an independent components method
14.40-15.00 Dr Ulykbek Kairov (Nazarbayev University) Analysis of human whole-transcriptome sequencing data from Illumina HiSeq2000 platform
15.00-15.20 Dr Niamh Forde (Faculty of Medicine, University of Leeds) Using βOmicsβ to understand successful early pregnancy events in cattle: The perspective of a reproductive biologist
15.20-16.00 Coffee break
16.00-17.00 Vladislav Govorkovskiy (Illumina, CIS) Application of Illumina NGS-technologies in healthcare, research and agriculture
Overview of NGS technologies and panel discussion on NGS equipment/sequencing platforms Chaired by Ian Carr (St Jamesβs Hospital, University of Leeds)
17.30-19.00 Poster session and speed networking
Weds 21st September
09.30-10.20 Plenary Dr Mary OβConnell (School of Biology, University of Leeds) Comparative genomics and Mechanisms of protein evolution
10.20-10.40 Dr Antonia Ford (School of Biological Sciences, University of Bangor) Genomic characterisation of wild tilapia populations
10.40-11.00 Ms Jennifer Stockdale (School of Biosciences, University of Cardiff) Hungry for more: Utilising Next Generation Sequencing to determine the dietary range of different species
11.00-11.30 Coffee break
11.30-11.50 Dr Elizabeth Duncan (School of Biology, University of Leeds) Understanding the molecular mechanisms of gene-environment interactions in insects
11.50-12.10 Dr Helen Hipperson (NERC Biomolecular Analysis Facility, University of Sheffield) Identifying genes affecting both adaptive divergence and reproductive isolation in Howea palms from Lord Howe Island using RNA-Seq
12.10-12.30 Dr Askhat Molkenov (Nazarbayev University) Peculiarities of bioinformatics processing and data conversion from Illumina HiSeq2000
12.30-12.50 Dr Deborah Dawson (NERC Biomolecular Analysis Facility, University of Sheffield) Support for biomolecular studies of the natural environment in the UK
12.50-14.00 Lunch
14.00-15.30 Discussion panel β designing and troubleshooting NGS projects Chaired by Morag Taylor (St Jamesβs Hospital, University of Leeds)
15.30-16.00 Coffee break
16.00-17.00 Discussion panel β designing and troubleshooting NGS projects continued
Thurs 22nd September
09.30-10.20 Plenary Dr Chris Knight (Faculty of Life Sciences, University of Manchester) Testing evolutionary mechanisms: mutation in microbes and more
10.20-10.40 Dr Saule Daugalieva (Institute of Microbiology and Virology) NGS 16S sequencing for microbial identification
10.40-11.00 Raushan Nugmanova (National Center for Biotechnology) Study of mutation clusters in bacteria using Ion Torrent sequencing
11.00-11.30 Coffee break
11.30-11.50 Dr Jenny Dunn (Royal Society for Protection of Birds) Using next-generation sequencing to examine co-infection and environmental parasite transmission
11.50-12.10 Dr Alexander Shevtsov (National Center for Biotechnology) NGS sequencing of veterinary pathogens
12.10-12.30 Dr Aizhan Turmagambetova (Institute of Microbiology and Virology) NGS for ecological research applications: macrophages profiling in Kazakhstan lakes
12.30-12.50 Dr Kobey Karamendin (Institute of Microbiology and Virology) NGS 16S sequencing of necropsy material from Saiga antelope after a mass die-off in Spring 2015
12.50-14.00 Lunch
14.00-15.30 NGS bioinformatics & data analysis resources and pipelines: Overview, demonstrations and panel discussion Chaired by Helen Hipperson (NERC Biomolecular Analysis Facility, University of Sheffield)
15.30-16.00 Coffee break
16.00-17.00 Bioinformatics discussion continued
19.00-23.00 Conference dinner
Fri 23rd September
09.30-10.00 Rowan Kennedy (Newton-Al Farabi Partnership Programme) UK-Kazakhstan research funding opportunities
10.00-10.30 Dr Simon Goodman (School of Biology, University of Leeds) Overview of research structure, funding and career development in the UK
10.30-11.00 Coffee break
11.00-12.00 Break out groups - Identification of research priorities and collaboration opportunities for UK-Kazakh researchers
12.00-12.30 Report of break out groups and closing remarks
12.30-14.00 Lunch
Departures
Posters
Author Title
Ulan Kozhamkulov Laboratory of Genomic and Personalized Medicine, Center for Life Sciences, National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
Whole genome sequencing of clinical isolates of M.tuberculosis with a different drug sensitivity profile on the Roche 454 GS FLX + platform
Ainur Akhmetova Laboratory of Genomic and Personalized Medicine, Center for Life Sciences, National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
Creating a HaloPlex cardiogenetic panel and preparation of DNA libraries for the targeted sequencing of patients with arrhythmias
Nurlan Torokeldiev Medical School of the International Ala-Too University in Bishkek
Pattern of genetic variation, fine-scale genetic structure and footprints of natural selection in populations of Juglans regia L. in the southern Kyrgyz Republic
Vladislav Govorkovskiy Illumina representative, Belarus
Poster about NGS technology
Vladislav Govorkovskiy Illumina representative, Belarus
Poster about production
Abstracts
Dr Ian M. Carr, St Jamesβs University Hospital, University of Leeds, UK
Diagnostic mutation detection using Next Generation Sequencing
Next generation sequencing (NGS) is a relatively new technology that can quickly and cheaply
generate huge amounts of sequence data. Consequently, it has rapidly found a wide range of
applications in both basic and translational research. These application range from de novo genome
assembly of large eukaryotic genomes to amplicon sequencing of huge cohorts. NGS also promises to
revolutionise diagnostic testing where it may prove cheaper than current testing methodologies, allow
the testing of low quality samples or allow the development of completely novel diagnostic tests.
In the UK, NGS technologies are seen as the future of many DNA based testing methodologies. The
Yorkshire Regional DNA Laboratory, in Leeds, was one of the first to offer NGS based diagnoses' and
has reported on over 6,000 cases. Currently, the Yorkshire Regional DNA Laboratory uses a range of
methodologies to identify mutations ranging from single base substitutions to large structural
rearrangements. I will discuss these advances in light of the population demographics in the Yorkshire
region and how the new tests are implemented alongside current best practises that it may either
replace or augment.
Dr Deborah Dawson, NERC NBAF Centre, University of Sheffield, UK
Support for biomolecular studies of the natural environment in the UK
In the UK, support is provided for molecular studies of the natural environment by the NERC
Biomolecular Analysis Facility. The Facility provides access to high-level genomics, metabolomics and
bioinformatics through its four nodes at Sheffield, Edinburgh, Liverpool and Birmingham.
The Facility offers the very latest, class-leading technologies, including next-generation sequencing
(Illumina and Pacific Biosciences), SNP genotyping, and high resolution MS and NMR metabolomic
platforms. Applications include de novo sequencing, metagenomics, epigenetics, sequence-capture,
sequencing-based genotyping and expression profiling (RNAseq, oligoarrays and NanoString). The
Facility also supports metabolomics, medium-scale genotyping, bioinformatics and advanced data
analysis techniques (genome and transcriptome assembly and annotation, expression analysis, etc.).
Each node takes the lead in providing support in one area. At Sheffield, access is provided to laboratory
facilities, equipment, training and expertise. The main call is for the development and application of
genetic markers for use in population genetics and behavioural ecology. We also support various other
techniques, including metabarcoding for genetic studies of diet. The service at Sheffield is based on a
well-proven arrangement, in which researchers visit the laboratory to complete their own analyses
under the supervision of someone experienced in the required technology. In most cases, the majority
of the bench work will be carried out by visitors to the Facility under the supervision of Facility staff.
Training is provided, as appropriate.
The Facility has supported over 200 projects and 150 PhD students. Our users have published over
300 publications from Facility-supported studies, including large numbers in high-ranking journals
such as Nature and Science.
Dr Elizabeth J. Duncan, School of Biology, Faculty of Biological Sciences, University of Leeds, UK
Understanding the molecular mechanisms of gene-environment interactions in insects.
The phenotype of a plant or animal is dependent on interactions between their genes and the
environment. Some plants and animals are even able to generate markedly different phenotypes in
response to a change in the environment, a phenomenon known as phenotypic plasticity.
Using the honeybee (Apis mellifera) and the pea aphid (Acyrthosiphon pisum) we have a developed
an analysis pipeline to begin to understand the molecular basis of how these gene-environment
interactions occur.
The honeybee and pea aphid both change the way they reproduce in response to changes in the
environment. In the honeybee hive only one female, the queen, usually reproduces. If the queen and
her pheromone are lost from the hive this triggers the normally sterile worker bees to become
reproductively active. Using a combination of techniques including RNA-seq to measure gene
expression and immunohistochemistry to determine which cell types in the ovary are affected we
have isolated a conserved signalling pathway as key to this process, Notch signalling. Among other
roles, Notch signalling has a key function in forming and maintaining stem cell niches and I propose
that these niches are key to gene-environment responses.
Epigenetic mechanisms, such as DNA methylation and histone modifications, also play a role in altering
the way animals respond to their environment and may also regulate stem-cell niches. To investigate
the role of epigenetic mechanisms in regulating the gene-environment interactions seen in the
honeybee I have used chromatin immunoprecipitation-sequencing (to investigate a particular histone
modification) and whole genome bisulphite sequencing to determine methylation patterns across the
genome.
Ultimately I aim to determine if there are conserved signalling pathways or regulatory networks that
control plasticity amongst diverse animals. Using these relatively simple and tractable systems to
understand the mechanisms of plasticity will allow us to understand, at a whole-organism level, how
animals are responding to their environment.
Jenny C. Dunn1, Rebecca C. Thomas2, Helen Hipperson3, Keith C. Hamer2 & Simon J. Goodman2
1 RSPB Centre for Conservation Science, Royal Society for the Protection of Birds, The Lodge, Potton
Road, Sandy, Bedfordshire, SG19 2DL, UK
2 School of Biology, Irene Manton Building, University of Leeds, Leeds. LS2 9JT, UK
3 NERC Biomolecular Analysis Facility, Department of Animal and Plant Sciences, University of
Sheffield, Western Bank, Sheffield, S10 2TN, UK
Using next-generation sequencing to examine co-infection and environmental parasite transmission
Co-infection with different parasites or multiple strains of the same parasite species is common in
natural systems and has implications for disease ecology and epidemiology. Traditional methods using
PCR either detect the dominant strain or return convoluted results from Sanger sequencing. Next-
generation sequencing (NGS) provides the opportunity to detect multiple strains of parasite
simultaneously from single samples, either from individuals or the environment. Here, I will describe
the application of NGS for parasite strain identification in a declining species of migratory bird, the
European Turtle Dove Streptopelia turtur. We screened blood samples and oral swabs for
haemoparasites and Trichomonas gallinae respectively, examining a single gene region (cytochrome
b) for haemoparasites, and two gene regions (ITS and FeDH) for Trichomonas gallinae. I will discuss
the laboratory methods and the bioinformatics analysis used, and discuss the applications of the
results in the context of ecology and conservation.
Dr Antonia G P Ford, School of Biological Sciences, Bangor University, Bangor, Gwynedd, LL57 2UW,
UK
Genomic characterisation of wild tilapia populations
Tilapia cichlid fish, and particularly the genus Oreochromis, are a mainstay of tropical aquaculture.
While most focus has been on strains of Nile tilapia (Oreochromis niloticus), several aquaculture
populations make use of hybrid lines and the ready hybridization of Oreochromis species. Future strain
enhancement may further benefit from the availability of additional wild genetic resources, which
have previously been used to enhance growth, environmental tolerance, control sex ratios, and
introduce genetic resistance to disease. However, existing native wild populations are frequently
poorly characterised and threatened by invasive tilapia species. Here, I will discuss an ongoing project
aiming to characterise wild populations of Oreochromis tilapia across a region of high cichlid
biodiversity, Tanzania, East Africa. Several introduced aquaculture tilapia species are found in wild
populations throughout Tanzania, where they are thought to compete with and hybridise with native
species. The project uses next generation sequencing (Illumina HiSeq) and SNP genotyping (Agena) to
survey wild populations to examine the extent and nature of introgression.
Dr Niamh Forde, Division of Reproduction and Early Development, Leeds Institute of Cardiovascular
and Metabolic Medicine, School of Medicine, University of Leeds, UK
Using βOmicsβ to understand successful early pregnancy events in cattle: The perspective of a
reproductive biologist.
In most mammalian species studied, the majority of pregnancy loss occurs in the first three weeks of
pregnancy. A large proportion of this loss can be attributed to asynchrony between the embryo and
the endometrium and or dysregulation of the uterine environment. A number of key events are
required to support successful early pregnancy in cattle. Specifically, an adequate post-ovulatory rise
in the hormone progesterone (P4) in circulation which to alter the endometrial transcriptome, an
appropriate uterine environment with the secretions required to drive embryo development as well
as appropriate pregnancy recognition signalling by the conceptus to the endometrium to maintain P4
concentrations in circulation and to establish uterine receptivity to implantation. The focus of my talk
will be on how we utelised βomicβ technologies to understand how the hormone progesterone alters
the ability of the uterus to support successful early pregnancy. In addition, I will demonstrate how
using RNA sequencing technologies helped us to identify an earlier pregnancy recognition response to
an embryo in the endometrium and proposed some biomarkers of early pregnancy in cattle. I will also
demonstrate how we have used RNA sequencing to look at how the metabolic environment of the
mother can have an impact on the transcriptome of lots of different reproductive tissues. Finally, I will
sum up the limitations and the pitfalls of using these types of technologies to address your biological
question.
Helen Hipperson, LT Dunning, WJ Baker, RK Butlin, C Devaux, I Hutton, J Igea, AST Papadopulos, X Quan, CM Smadja, CGN Turnbull, TC Wilson, VS Savolainen
NERC NBAF Centre, University of Sheffield, UK
Identifying genes affecting both adaptive divergence and reproductive isolation in Howea palms from Lord Howe Island using RNA-Seq
Howea belmoreana and Howea forsteriana are sister species of palm, both endemic to Lord Howe Island (LHI; located in the Tasman Sea between Australia and New Zealand) where they have diverged in sympatry. Originally composed solely of volcanic substrate, the deposition of calcareous soil on LHI is thought to have led to ecological speciation. Currently, H. belmoreana adults are restricted to volcanic soils whilst H. forsteriana is also found on the younger calcarenite soil. There are several ecological differences between these habitats; the calcareous soils are dryer, have higher pH, and have increased salinity compared to volcanic soil. The species are largely reproductively isolated with a five week difference in peak flowering time between them, both in the wild and when cultivated in a common garden. Differences in the peak flowering times are also maintained regardless of the soil type that H. forsteriana occurs on. Genes that have a dual role in controlling ecological adaptation and flowering time may have played a direct role in Howea speciation. To characterise such pleiotropic genes we first used RNA-Seq to identify differentially expressed genes between the Howea species using three tissue types (floral, leaf and root) sampled from 36 trees distributed across LHI. We also examined loci with divergent coding sequences. From both analyses we identified 16 candidate genes that were associated with ecological differences between the species and/or flowering time divergence, and examined the effect that eight of these genes have on flowering time in Arabidopsis knockout mutants. Finally, we put forward six plausible ecological speciation loci, providing support for the hypothesis that pleiotropy could help to overcome the antagonism between selection and recombination during speciation with gene flow.
Dr Mary OβConnell, School of Biology, Faculty of Biological Sciences, University of Leeds, UK
Comparative genomics and Mechanisms of protein evolution
The relationship between sequence and function has proven difficult to fully elucidate but it is key to
understanding what makes a species unique. Here I will describe how we can help to bridge the gaps
in our understanding of the relationships (i) between species and (ii) between genotype and
phenotype, by adequately modelling major patterns in genomic sequence data. I will present results
from a small selection of large-scale comparative genomics studies and I will describe our approach
for identifying the evolution of species-specific proteins/protein functions using genome-scale data
and computational evolutionary models. Taking an applied evolutionary approach to modelling may
provide us with an increased understanding of species-specific response to disease/drugs at the
molecular level.
Dr Chris Knight, Faculty of Life Sciences, University of Manchester
Testing evolutionary mechanisms: mutation in microbes and more
Tackling major global challenges, such as the rise in antimicrobial resistance, requires a focus on the
fundamental evolutionary processes that underlie them. We are experimentally testing the
spontaneous evolution of antibiotic resistance in different microbes. Mutation rates have been
measured using phenotypic markers, including antibiotic resistances, for over 70 years. We find
patterns in this data suggesting that dense populations may evolve resistance at a lower rate than
sparse populations. Manipulating population densities in the laboratory, in either bacteria or yeast,
we can modify the mutation rate to several different antibiotic resistances by over an order of
magnitude. We find that this βdensity associated mutation rate plasticityβ (DAMP) requires an
evolutionarily ancient mutation avoidance mechanism, but is modified or mediated in particular
lineages, including by cell-cell interactions with the surrounding community. The next level is
therefore to consider the evolution of mixed microbial communities. We are considering both
experimental (mouse gut) and broader microbial meta-genetic data (soil communities), where we are
using novel approaches to distinguish the biologically interesting signals from a range of technical
confounders. Through a combination of modelling approaches and next generation sequence data we
are gaining a closer connection between our understandings of genotypic change, phenotype and
ecology. This will contribute to addressing major issues, including antimicrobial resistance, but at the
same time help shed new, molecular, light on classically understood evolutionary processes.
Jennifer Stockdale1, Jenny Dunn1,2, Joanna Redihough1, Helen Hipperson3, William Symondson1
1Cardiff University School of Biosciences, Sir Martin Evans Building, Museum Avenue, Cardiff, CF10
3AX.
2RSPB Centre for Conservation Science, The Lodge, Sandy, Bedfordshire. SG19 2DL
3 NERC Biomolecular Analysis Facility, Department of Animal and Plant Sciences, University of
Sheffield, Western Bank, Sheffield, S10 2TN, UK
Hungry for more: Utilising next generation sequencing to determine the dietary range of different
species.
Next generation sequencing (NGS) is increasingly being used to look at the complete dietary range of
species. To date, there have been ecological analyses using molecular scatology to study the diets of
killer whales and leopards at one end of the spectrum, with specialist termite-eating spiders at the
other. Molecular analyses consequently tend to be replacing more traditional techniques of
morphological analysis of faecal samples, stomach flushing, nest cameras and direct observation to
identify dietary components. At Cardiff University we are using NGS to determine the dietary ranges
of invertebrates and vertebrates in both temperature and tropical habitats. I will discuss three
ongoing projects examining diets of the Common Crane (which eats invertebrates and plants),
thrushes in farmland (which eat invertebrates), and the European Turtle Dove (which eats plant
seeds). Work on the recently reintroduced Common Crane to the Somerset levels will provide new
insight into Eurasian Crane diet, which may aid any potential future reintroductions and will help to
sustain these birds in Britain. NGS is also being used to monitor the diets of thrushes in farmland
landscapes of different complexity enabling us to link the prey found in their faeces with the use of
landscape elements (arable field, woodland etc.). Finally, we have been able to determine implications
of diet for Turtle Dove body condition, consider changes in diet over time and usage of bespoke habitat
management options with implications for conservation management.
Morag Taylor, Susan Richman, Tim Palmer, Henry Wood, Caroline Young, Phil Quirke
St Jamesβs University Hospital, University of Leeds, UK
The Role of Next Generation Sequencing in Colorectal Cancer Research
In the United Kingdom (UK), colorectal cancer (CRC) is the fourth most common cancer, and the
second most common cause of cancer related deaths. Itβs important to understand both the
prognostic and predictive markers of CRC to improve these statistics. The advent of next generation
sequencing (NGS) is allowing us to explore tumour profiling and mutation screening in new ways,
advancing the role of molecular pathology.
We are part of several multicentre clinical trials investigating CRC biomarkers to determine patient
treatment. Until now, we have favoured the lower cost sequencing alternatives, but with the
continuing reduction in sequencing costs, and the need to adapt assays quickly whilst keeping
technical time down, we are developing a pipeline to move our clinical trials to NGS.
We have investigated genomic heterogeneity in CRC. Using copy number variation data from NGS, we
have analysed the primary tumours and all distant metastases from eight patients who died of
advanced CRC. We generated phylogenetic trees for each patient to follow the evolution of the
disease.
The human microbiome has been studied for many years, but NGS has made this study area more
accessible. It has allowed for the identification of bacteria that were previously un-culturable. Studies
have shown the gut microbiome plays a role in CRC, but as yet, this isnβt fully understood. In order to
investigate this, we are developing a pipeline to study the human microbiome isolated from guaiac
faecal occult blood test cards, used worldwide to screen for CRC. We will follow this by doing a UK
study to investigate if the microbiome can be used as a future tool to predict CRC using 16s rRNA
sequencing on an Illumina MiSeq.
Ainur Akilzhanova
Laboratory of Genomic and Personalized Medicine, Center for Life Sciences, National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
Genomic research in Kazakhstan: challenges and opportunities for clinical applications
Technological advancements are rapidly propelling the field of genome research forward. Advances in genetics and genomics such as the sequence of the human genome, the human haplotype map, open access databases, cheaper genotyping and chemical genomics have already transformed basic and translational biomedical research. At the National Laboratory Astana (NLA), Center for Life Sciences, Nazarbayev University several projects in the field of genomic and personalized medicine are conducting. The prioritized areas of research include genomics of multifactorial diseases, cancer genomics, bioinformatics, genetics of infectious diseases and population genomics. At present, DNA-based risk assessment for common complex diseases, application of molecular signatures for cancer
diagnosis and prognosis, genome-guided therapy, and dose selection of therapeutic drugs are the important issues in personalized medicine.
Kazakhstan is a unique country located in the middle of Central Asia, laying on the ancient Great Silk road. Kazakh populations have been strongly influenced by the nomadic lifestyle, and a long history of migration has led to admixture of western and Asian populations, which has molded the genetic architecture. Thus it is crucial to understand the genetic background of ethnic Kazakhs to properly investigate the genetic basis of common diseases or traits in Kazakh populations. To develop a personalized medicine program for Kazakhstan, we first need acquire personal genomic data for Kazakhs. To do so, we need a core of scientists who can: (1) design proper studies; (2) diagnose accurately; (3) sequence efficiently (using multiomic technologies); (4) analyze and maintain massive sequence data; (5) analyze the relations between genetic variants and phenotypes (i.e., disease status or biomarkers.
To further develop genomic and biomedical projects at NLA and in Kazakhstan the development of bioinformatics research and infrastructure is essential, as well as establishment of new collaborations in this field.
Widespread use of genetic tools will allow the identification of diseases before the onset of clinical symptoms, the individualization of drug treatment, and could induce individual behavioral changes on the basis of calculated disease risk. However, many challenges remain for the successful translation of genomic knowledge and technologies into health advances, such as medicines and diagnostics.
It is important to integrate research and education in the fields of genomics, personalized medicine and bioinformatics which will be possible with opening of new Medical Faculty in Nazarbayev University. Educating both those in practice and those in training about key concepts of genomics and, importantly, engaging them in the design of how this knowledge will be applied most effectively will rapidly bring the era of genomic medicine to patient care, resulting in improved health. And all of this must be based on good research and scientific platform which requires development of well-equipped modern laboratories, bioinformatics, qualified trained physicians and laboratory staff and understanding policy among population of the country.
ΠΠ΅Π½ΠΎΠΌΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π² ΠΠ°Π·Π°Ρ ΡΡΠ°Π½Π΅: ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π΄Π»Ρ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ
Π’Π΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΡ Π΄Π΅ΡΡΡΠΈΠ»Π΅ΡΠΈΠΉ ΠΏΡΠΎΠ΄Π²ΠΈΠ½ΡΠ»ΠΈ ΠΎΠ±Π»Π°ΡΡΡ Π³Π΅Π½ΠΎΠΌΠ½ΡΡ ΠΈ ΠΌΡΠ»ΡΡΠΈΠΎΠΌΠ½ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π²ΠΏΠ΅ΡΠ΅Π΄. ΠΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΡ Π² ΠΎΠ±Π»Π°ΡΡΠΈ Π³Π΅Π½Π΅ΡΠΈΠΊΠΈ ΠΈ Π³Π΅Π½ΠΎΠΌΠΈΠΊΠΈ, ΡΠ°ΠΊΠΈΠ΅ ΠΊΠ°ΠΊ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π³Π΅Π½ΠΎΠΌΠ° ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°, ΠΊΠ°ΡΡΡ Π³Π°ΠΏΠ»ΠΎΡΠΈΠΏΠΎΠ² ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°, Π±Π°Π·Ρ Π΄Π°Π½Π½ΡΡ ΠΎΡΠΊΡΡΡΠΎΠ³ΠΎ Π΄ΠΎΡΡΡΠΏΠ°, ΡΠ΄Π΅ΡΠ΅Π²Π»Π΅Π½ΠΈΠ΅ Π³Π΅Π½ΠΎΡΠΈΠΏΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ Ρ ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ Π³Π΅Π½ΠΎΠΌΠΈΠΊΠΈ ΡΠΆΠ΅ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π»ΠΈ ΡΡΠ½Π΄Π°ΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΈ ΡΡΠ°Π½ΡΠ»ΡΡΠΈΠΎΠ½Π½ΡΠ΅ Π±ΠΈΠΎΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ. Π National Laboratory Astana (NLA), Π¦Π΅Π½ΡΡΠ΅ Π½Π°ΡΠΊ ΠΎ ΠΆΠΈΠ·Π½ΠΈ ΠΠ°Π·Π°ΡΠ±Π°Π΅Π² Π£Π½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ° ΡΠ΅Π°Π»ΠΈΠ·ΡΡΡΡΡ ΠΏΡΠΎΠ΅ΠΊΡΡ Π² ΠΎΠ±Π»Π°ΡΡΠΈ Π³Π΅Π½ΠΎΠΌΠ½ΠΎΠΉ ΠΈ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Ρ. ΠΡΠΈΠΎΡΠΈΡΠ΅ΡΠ½ΡΠ΅ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ: Π³Π΅Π½ΠΎΠΌΠΈΠΊΠ° ΠΌΠ½ΠΎΠ³ΠΎΡΠ°ΠΊΡΠΎΡΠ½ΡΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ, ΡΠ°ΠΊΠ°, Π±ΠΈΠΎΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΊΠ° ΠΈ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½Π°Ρ ΡΠΈΡΡΠ΅ΠΌΠ½Π°Ρ Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡ, Π³Π΅Π½Π΅ΡΠΈΠΊΠ° ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΎΠ½Π½ΡΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΈ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΎΠ½Π½Π°Ρ Π³Π΅Π½ΠΎΠΌΠΈΠΊΠ°, ΠΌΡΠ»ΡΡΠΈΠΎΠΌΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ, ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΠΈ Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΠ΅ NGS ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π² ΠΏΡΠ°ΠΊΡΠΈΠΊΡ. Π Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ ΠΎΡΠ΅Π½ΠΊΠ° ΡΠΈΡΠΊΠ° ΠΎΠ±ΡΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΠΠΠ, ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΡΡ ΡΠΈΠ³Π½Π°ΡΡΡ Π΄Π»Ρ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΡΠ°ΠΊΠ°, Π³Π΅Π½ΠΎΠΌ Π°ΡΡΠΎΡΠΈΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠ΅ΡΠ°ΠΏΠΈΠΈ ΠΈ ΠΏΠΎΠ΄Π±ΠΎΡΠ΅ Π΄ΠΎΠ·Ρ ΡΠ΅ΡΠ°ΠΏΠ΅Π²ΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠΎΠ² ΡΠ²Π»ΡΡΡΡΡ Π²Π°ΠΆΠ½ΡΠΌΠΈ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌΠΈ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Ρ.
ΠΠ°Π·Π°Ρ ΡΡΠ°Π½ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ½ΠΈΠΊΠ°Π»ΡΠ½ΠΎΠΉ ΡΡΡΠ°Π½ΠΎΠΉ, ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½Π½ΠΎΠΉ Π² ΡΠ΅Π½ΡΡΠ΅ Π¦Π΅Π½ΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΠ·ΠΈΠΈ, Π»Π΅ΠΆΠΈΡ Π½Π° Π΄ΡΠ΅Π²Π½Π΅ΠΌ ΠΠ΅Π»ΠΈΠΊΠΎΠΌ ΡΠ΅Π»ΠΊΠΎΠ²ΠΎΠΌ ΠΏΡΡΠΈ. ΠΠ°Π·Π°Ρ ΡΡΠ°Π½ΡΠΊΠΎΠ΅ Π½Π°ΡΠ΅Π»Π΅Π½ΠΈΠ΅ Π±ΡΠ»ΠΎ ΠΏΠΎΠ΄ ΡΠΈΠ»ΡΠ½ΡΠΌ Π²Π»ΠΈΡΠ½ΠΈΠ΅ΠΌ ΠΊΠΎΡΠ΅Π²ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·Π° ΠΆΠΈΠ·Π½ΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΠΌΠ΅Π΅Ρ Π΄ΠΎΠ»Π³ΡΡ ΠΈΡΡΠΎΡΠΈΡ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΈ, ΡΡΠΎ ΠΏΡΠΈΠ²Π΅Π»ΠΎ ΠΊ ΡΠΌΠ΅ΡΠΈ Π·Π°ΠΏΠ°Π΄Π½ΡΡ ΠΈ Π°Π·ΠΈΠ°ΡΡΠΊΠΈΡ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΉ, ΠΊΠΎΡΠΎΡΡΠ΅ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π»ΠΈ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΡΡ Π°ΡΡ ΠΈΡΠ΅ΠΊΡΡΡΡ Π½Π°ΡΠΎΠ΄Π°. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, ΠΊΡΠ°ΠΉΠ½Π΅ Π²Π°ΠΆΠ½ΠΎ, ΡΡΠΎΠ±Ρ ΠΏΠΎΠ½ΡΡΡ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΡΠΎΠ½ ΡΡΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ ΠΊΠ°Π·Π°Ρ ΠΎΠ², ΡΡΠΎΠ±Ρ Π΄ΠΎΠ»ΠΆΠ½ΡΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΡ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΡΡ ΠΎΡΠ½ΠΎΠ²Ρ ΠΎΠ±ΡΠΈΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΈΠ»ΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Ρ ΠΊΠ°Π·Π°Ρ ΡΠΊΠΎΠ³ΠΎ Π½Π°ΡΠ΅Π»Π΅Π½ΠΈΡ. ΠΠ»Ρ ΡΠΎΠ³ΠΎ, ΡΡΠΎΠ±Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°ΡΡ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Ρ Π΄Π»Ρ ΠΠ°Π·Π°Ρ ΡΡΠ°Π½Π°, ΠΌΡ Π² ΠΏΠ΅ΡΠ²ΡΡ ΠΎΡΠ΅ΡΠ΅Π΄Ρ Π½Π΅ΠΎΠ±Ρ ΠΎΠ΄ΠΈΠΌΠΎ ΠΏΠΎΠ»ΡΡΠΈΡΡ Π»ΠΈΡΠ½ΡΠ΅ Π΄Π°Π½Π½ΡΠ΅ Π³Π΅Π½ΠΎΠΌΠΎΠ² ΠΊΠ°Π·Π°Ρ ΡΡΠ°Π½ΡΠ΅Π². ΠΠ»Ρ ΡΡΠΎΠ³ΠΎ Π½ΡΠΆΠ΅Π½ ΠΏΡΠ» ΡΡΠ΅Π½ΡΡ , ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠ³ΡΡ: (1) ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°ΡΡ ΠΈ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΡΡ Π½Π°Π΄Π»Π΅ΠΆΠ°ΡΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ; (2) ΡΠΎΡΠ½ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°ΡΡ; (3) ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΡΡ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΡΠ»ΡΡΠΈΠΎΠΌΠΈΠΊΡΠ½ΡΡ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ; (4) Π°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΠΈ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΈΠ²Π°ΡΡ Π±ΠΎΠ»ΡΡΠΈΠ΅ ΠΌΠ°ΡΡΠΈΠ²Ρ Π΄Π°Π½Π½ΡΡ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ; (5) Π°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΌΠ΅ΠΆΠ΄Ρ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ Π²Π°ΡΠΈΠ°Π½ΡΠ°ΠΌΠΈ ΠΈ ΡΠ΅Π½ΠΎΡΠΈΠΏΠ°ΠΌΠΈ (Ρ.Π΅. ΡΡΠ°ΡΡΡΠΎΠΌ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ ΠΈΠ»ΠΈ Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠ°ΠΌΠΈ) ΠΈ Π΄Ρβ¦
Π ΡΠ΅Π»ΡΡ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅Π³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π³Π΅Π½ΠΎΠΌΠ½ΡΡ ΠΈ Π±ΠΈΠΎΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π² NLA ΠΈ Π² ΠΠ°Π·Π°Ρ ΡΡΠ°Π½Π΅ ΠΈΠΌΠ΅Π΅Ρ Π²Π°ΠΆΠ½ΠΎΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ ΠΈΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΡ ΠΈ Π±ΠΈΠΎΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΊΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π°Π»Π°ΠΆΠΈΠ²Π°Π½ΠΈΠ΅ ΡΠΎΡΡΡΠ΄Π½ΠΈΡΠ΅ΡΡΠ²Π° Ρ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΠΌΠΈ Π»Π°Π±ΠΎΡΠ°ΡΠΎΡΠΈΡΠΌΠΈ ΠΈ ΠΊΠΎΠ½ΡΠΎΡΡΠΈΡΠΌΠ°ΠΌΠΈ Π² ΡΡΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ, Ρ ΠΊΠ»ΠΈΠ½ΠΈΠΊΠ°ΠΌΠΈ ΠΈ Π²ΡΠ·Π°ΠΌΠΈ ΠΈ Π½Π°ΡΡΠ½ΡΠΌΠΈ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠ²Π°ΠΌΠΈ.
Π¨ΠΈΡΠΎΠΊΠΎΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΎΠ² ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΡ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°ΡΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ Π΄ΠΎ ΠΏΠΎΡΠ²Π»Π΅Π½ΠΈΡ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ ΡΠΈΠΌΠΏΡΠΎΠΌΠΎΠ², ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π»Π΅ΠΊΠ°ΡΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΡΠ΅ΡΠ°ΠΏΠΈΠΈ, ΠΈ ΠΌΠΎΠΆΠ΅Ρ Π²ΡΠ·Π²Π°ΡΡ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅
ΡΠ°ΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΡΠΊΠ° Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ. Π’Π΅ΠΌ Π½Π΅ ΠΌΠ΅Π½Π΅Π΅, ΠΌΠ½ΠΎΠ³ΠΈΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΎΡΡΠ°ΡΡΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΠΌΠΈ Π΄Π»Ρ ΡΡΠΏΠ΅ΡΠ½ΠΎΠ³ΠΎ ΠΏΠ΅ΡΠ΅Π²ΠΎΠ΄Π° Π³Π΅Π½ΠΎΠΌΠ½ΡΡ Π·Π½Π°Π½ΠΈΠΉ, ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΠΈ Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΠΉ Π² ΠΏΡΠ°ΠΊΡΠΈΠΊΡ Π² ΠΎΠ±Π»Π°ΡΡΠΈ Π·Π΄ΡΠ°Π²ΠΎΠΎΡ ΡΠ°Π½Π΅Π½ΠΈΡ.
ΠΠ°ΠΆΠ½ΠΎ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΡΡ ΠΈΠ½ΡΠ΅Π³ΡΠ°ΡΠΈΡ Π½Π°ΡΡΠ½ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΈ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π² ΠΎΠ±Π»Π°ΡΡΠΈ Π³Π΅Π½ΠΎΠΌΠΈΠΊΠΈ, ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Ρ ΠΈ Π±ΠΈΠΎΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΊΠΈ, ΠΊΠΎΡΠΎΡΡΠΉ ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΠΌ Ρ ΠΎΡΠΊΡΡΡΠΈΠ΅ΠΌ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠ³ΠΎ ΡΠ°ΠΊΡΠ»ΡΡΠ΅ΡΠ° Π² ΠΠ°Π·Π°ΡΠ±Π°Π΅Π² Π£Π½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ΅. ΠΠ±ΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΠΊ Π½Π° ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅, ΡΠ°ΠΊ ΠΈ Π² ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΠΌ ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΊΠ»ΡΡΠ΅Π²ΡΠΌ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΈΡΠΌ Π³Π΅Π½ΠΎΠΌΠ½ΠΎΠΉ ΠΈ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Ρ ΠΈ, ΡΡΠΎ Π½Π΅ΠΌΠ°Π»ΠΎΠ²Π°ΠΆΠ½ΠΎ, Π²ΠΎΠ²Π»Π΅ΡΠ΅Π½ΠΈΠ΅ Π² Π΄ΠΈΠ·Π°ΠΉΠ½, ΠΊΠ°ΠΊ ΡΡΠΎ Π·Π½Π°Π½ΠΈΠ΅ Π±ΡΠ΄Π΅Ρ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡΡΡ Π² ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅, ΠΌΠΎΠ³ΡΡ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΠΎΠ²Π°ΡΡ Π±ΠΎΠ»Π΅Π΅ Π±ΡΡΡΡΠΎΠΌΡ Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΡ ΡΡΡ Π³Π΅Π½ΠΎΠΌΠ½ΠΎΠΉ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Ρ Π² ΠΠ°Π·Π°Ρ ΡΡΠ°Π½Π΅, ΡΡΠΎ ΠΌΠΎΠΆΠ΅Ρ ΠΏΡΠΈΠ²Π΅ΡΡΠΈ ΠΊ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ Π·Π΄ΠΎΡΠΎΠ²ΡΡ Π½Π°ΡΠΎΠ΄Π°. Π Π²ΡΠ΅ ΡΡΠΎ Π΄ΠΎΠ»ΠΆΠ½ΠΎ Π±ΡΡΡ ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΎ Π½Π° Ρ ΠΎΡΠΎΡΠ΅ΠΉ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΎΠΉ ΠΈ Π½Π°ΡΡΠ½ΠΎΠΉ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠ΅, ΠΊΠΎΡΠΎΡΠ°Ρ ΡΡΠ΅Π±ΡΠ΅Ρ Ρ ΠΎΡΠΎΡΠΎ ΠΎΠ±ΠΎΡΡΠ΄ΠΎΠ²Π°Π½Π½ΡΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ Π»Π°Π±ΠΎΡΠ°ΡΠΎΡΠΈΠΉ, ΡΠ°Π·Π²ΠΈΡΠΈΡ Π±ΠΈΠΎΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΊΠΈ, Π½Π°Π»ΠΈΡΠΈΡ ΠΊΠ²Π°Π»ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²Π»Π΅Π½Π½ΡΡ Π²ΡΠ°ΡΠ΅ΠΉ ΠΈ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»Π° Π»Π°Π±ΠΎΡΠ°ΡΠΎΡΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²Π»Π΅Π½Π½ΠΎΠ³ΠΎ Π½Π°ΡΠ΅Π»Π΅Π½ΠΈΡ ΡΡΡΠ°Π½Ρ.
Ulykbek Kairov
Laboratory of Bioinformatics and Computational Systems Biology, Center for Life Sciences,
National Laboratory Astana, Nazarbayev University
Analysis of human whole-transcriptome sequencing data from Illumina HiSeq2000 platform
The high-throughput genomic technologies and particularly Illumina HiSeq2000 next-generation
sequencing platform have a major impact on studying cancer. Illumina HiSeq2000 NGS platform
generating up to 600 GB of sequencing data per run. Huge amount of sequencing data requires
application of reproducible bioinformatics methods, mathematical and statistical approaches for
analysis. Transcriptomic profiling of cancer specimens with Illumina HiSeq2000 NGS platform has
provided a comprehensive opportunity for in-depth investigation of gene expression and affected
molecular pathways. In our study we aimed to perform comprehensive analysis of sequencing data
from HiSeq2000 platform to identify affected molecular pathways and extract meaningful molecular
signals from oesophageal cancer specimens of Kazakhstani patients.
ΠΠ½Π°Π»ΠΈΠ· Π΄Π°Π½Π½ΡΡ ΠΏΠΎΠ»Π½ΡΡ ΡΡΠ°Π½ΡΠΊΡΠΈΠΏΡΠΎΠΌΠΎΠ² Ρ ΠΏΠ»Π°ΡΡΠΎΡΠΌΡ ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ
Illumina HiSeq2000.
ΠΡΡΠΎΠΊΠΎΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ Π³Π΅Π½ΠΎΠΌΠ½ΡΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ, Π² ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ, ΠΏΠ»Π°ΡΡΠΎΡΠΌΠ° ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ
Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ Illumina HiSeq2000, ΡΠ²Π»ΡΡΡΡΡ Π·Π½Π°ΡΠΈΠΌΡΠΌΠΈ Π² ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΌ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠΈ
ΠΎΠ½ΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ. ΠΠ»Π°ΡΡΠΎΡΠΌΠ° ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ Illumina HiSeq2000
Π³Π΅Π½Π΅ΡΠΈΡΡΠ΅Ρ Π΄ΠΎ 600 ΠΠ± Π΄Π°Π½Π½ΡΡ Π·Π° ΠΎΠ΄ΠΈΠ½ Π·Π°ΠΏΡΡΠΊ. ΠΠ΅Π½Π΅ΡΠΈΡΡΠ΅ΠΌΡΠ΅ ΠΎΠ³ΡΠΎΠΌΠ½ΡΠ΅ ΠΌΠ°ΡΡΠΈΠ²Ρ Π΄Π°Π½Π½ΡΡ ΡΡΠ΅Π±ΡΡΡ
ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠΌΡΡ Π±ΠΈΠΎΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈ Π½Π΅ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΡ
ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΏΠΎΠ΄Ρ ΠΎΠ΄ΠΎΠ² Π°Π½Π°Π»ΠΈΠ·Π°. Π’ΡΠ°Π½ΡΠΊΡΠΈΠΏΡΠΎΠΌΠ½ΠΎΠ΅ ΠΏΡΠΎΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅
ΠΎΠΏΡΡ ΠΎΠ»Π΅Π²ΡΡ ΠΎΠ±ΡΠ°Π·ΡΠΎΠ² Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΏΠ»Π°ΡΡΠΎΡΠΌΡ Illumina HiSeq2000 NGS ΠΎΡΠΊΡΡΠ²Π°Π΅Ρ Π½ΠΎΠ²ΡΠ΅
Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ ΠΌΠ°ΡΡΡΠ°Π±Π½ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΊΡΠΏΡΠ΅ΡΡΠΈΠΈ ΠΈ ΠΏΠΎΠΈΡΠΊΠ° ΠΊΠ»ΡΡΠ΅Π²ΡΡ
ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΡΡ ΡΠ΅ΡΠ΅ΠΉ. ΠΠ°ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΎ Π½Π° ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ Π²ΡΠ΅ΡΡΠΎΡΠΎΠ½Π½Π΅Π³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°
Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΊΡΠΏΡΠ΅ΡΡΠΈΠΈ Π΄Π»Ρ ΠΏΠΎΠΈΡΠΊΠ° ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΡΡ ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Π² ΡΡΠ°Π½ΡΠΊΡΠΈΠΏΡΠΎΠΌΠ½ΡΡ ΠΏΡΠΎΡΠΈΠ»ΡΡ
ΠΊΠ°Π·Π°Ρ ΡΡΠ°Π½ΡΠΊΠΈΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ Π΄ΠΈΠ°Π³Π½ΠΎΠ·ΠΎΠΌ ΡΠ°ΠΊ ΠΏΠΈΡΠ΅Π²ΠΎΠ΄Π°.
Ulykbek Kairov
Laboratory of Bioinformatics and Computational Systems Biology, Center for Life Sciences, National
Laboratory Astana, Nazarbayev University
Meta-analysis of cancer transcriptome profiles using Independent Component Analysis
The high-throughput genomic technologies such a microarray technology and next-generation
sequencing have a major impact on studying cancer. Huge amount of genomic data requires
application of reproducible analytical approaches. In our study we demonstrated application of
Independent Component Analysis method to do meta-analysis of breast cancer gene expression data.
We identified from 7 to 8 reproducible components in all four breast cancer datasets and developed
graph-based approach to meta-analysis and interpretation of these independent components such
that each of them was associated with a small gene network. Using analysis of these networks, we
provided a tentative interpretation of stably reproducible components. Thus, we found that various
factors such as proliferation, immune response, contamination of tumor cells by lymphocytes and
normal tissues affect gene expression in breast cancer.
ΠΠ΅ΡΠ°-Π°Π½Π°Π»ΠΈΠ· ΡΠ°ΠΊΠΎΠ²ΡΡ ΡΡΠ°Π½ΡΠΊΡΠΈΠΏΡΠΎΠΌΠΎΠ² Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΠ΅ΡΠΎΠ΄Π° ΠΠ΅Π·Π°Π²ΠΈΡΠΈΠΌΡΡ ΠΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ.
ΠΡΡΠΎΠΊΠΎΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ Π³Π΅Π½ΠΎΠΌΠ½ΡΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ, ΡΠ°ΠΊΠΈΠ΅ ΠΊΠ°ΠΊ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ Π²ΡΡΠΎΠΊΠΎΠΏΠ»ΠΎΡΠ½ΡΡ
ΠΌΠΈΠΊΡΠΎΡΠΈΠΏΠΎΠ² ΠΈ ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ Illumina HiSeq2000, Π²Π½ΠΎΡΡΡ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΡΠΉ
Π²ΠΊΠ»Π°Π΄ Π² ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ΅ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΠΎΠ½ΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ. ΠΠ³ΡΠΎΠΌΠ½ΡΠ΅ ΠΌΠ°ΡΡΠΈΠ²Ρ Π³Π΅Π½ΠΎΠΌΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ ΡΡΠ΅Π±ΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠΌΡΡ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ². Π Π½Π°ΡΠ΅ΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΈ
ΠΌΡ ΠΏΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΠΎΠ²Π°Π»ΠΈ ΡΠΏΠΎΡΠΎΠ±ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΠ΅ΡΠΎΠ΄Π° ΠΠ΅Π·Π°Π²ΠΈΡΠΈΠΌΡΡ ΠΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ Π΄Π»Ρ ΠΌΠ΅ΡΠ°-
Π°Π½Π°Π»ΠΈΠ·Π° Π½Π°Π±ΠΎΡΠΎΠ² Π΄Π°Π½Π½ΡΡ Ρ ΡΠ°ΠΊΠΎΠΌ ΠΌΠΎΠ»ΠΎΡΠ½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Ρ. ΠΡΠ»ΠΎ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΎ ΠΎΡ 7 Π΄ΠΎ 8
Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠΌΡΡ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ Π²ΠΎ Π²ΡΠ΅Ρ Π½Π°Π±ΠΎΡΠ°Ρ ΡΠ°ΠΊΠ° ΠΌΠΎΠ»ΠΎΡΠ½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Ρ ΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π»ΠΈ ΠΏΠΎΠ΄Ρ ΠΎΠ΄ Ρ
ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ΅ΠΎΡΠΈΠΈ Π³ΡΠ°ΡΠΎΠ² Π΄Π»Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΌΠ΅ΡΠ°-Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠ°ΡΠΈΠΈ Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΡΡ
ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ.
Saule Rakhimova
National Laboratory Astana, Nazarbayev University)
Transcriptome profiling of oesophageal cancer: from biomaterial sampling to sequencing on
HiSeq2000.
The report presents the study of transcriptome profile of esophageal squamous cell carcinoma using
NGS technology. Description of work includes the following steps: sampling of biological material,
nucleic acids isolation, library preparation, library validation methods used in the laboratory.
Esophageal cancer is the sixth common cancer in Kazakhstan, and usually not detected until it has
progressed to an advanced incurable stage. More than 80% of the cancer cases and deaths occur in
developing countries and Central and East Asia. Aim of study: to identify genetic basis of esophageal
cancer by performing whole human transcriptome sequencing study in Kazakhstan.
Patient recruitment was carried out on the Thoracic surgery department, Oncology Center, Astana.
We include only patient with confirmed informed consent and confirmed diagnosis of esophageal
squamous cell carcinoma, to whom was performed radical surgery (Ivor Lewis esophagectomy), and
was available blood analysis, biochemical data, CT, X-ray, histopathological data.
Materials: pairs of freshly frozen (after RNA later solution) esophageal cancer tissue specimen and
normal tissue specimen.
Methods: RNA isolation, Library preparation, Library validation, Hybridization on flow cell, Sequencing
on HiSeq 2000.
For RNA isolation and purification was used Qiagen kits, for library preparation was used Tru Seq RNA
sample preparation kit, all procedures were performed according to Illumina protocols.
Π‘Π°ΡΠ»Π΅ Π Π°Ρ ΠΈΠΌΠΎΠ²Π° (Π§Π£ Β«National Laboratory AstanaΒ», ΠΠ°Π·Π°ΡΠ±Π°Π΅Π² Π£Π½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅Ρ) β Π’ΡΠ°Π½ΡΠΊΡΠΈΠΏΡΠΎΠΌΠ½ΡΠΉ
ΠΏΡΠΎΡΠΈΠ»Ρ ΡΠ°ΠΊΠ° ΠΏΠΈΡΠ΅Π²ΠΎΠ΄Π°: ΠΎΡ Π·Π°Π±ΠΎΡΠ° ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π° Π΄ΠΎ ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π° HiSeq2000.
Π Π΄ΠΎΠΊΠ»Π°Π΄Π΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ NGS ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ Π² Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΠΈ Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠ° ΠΏΠΎ
ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΡΡΠ°Π½ΡΠΊΡΠΈΠΏΡΠΎΠΌΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠΈΠ»Ρ ΠΏΠ»ΠΎΡΠΊΠΎΠΊΠ»Π΅ΡΠΎΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΠΊΠ° ΠΏΠΈΡΠ΅Π²ΠΎΠ΄Π°. ΠΠΏΠΈΡΠ°Π½ΠΈΠ΅ ΡΠ°Π±ΠΎΡΡ
Π²ΠΊΠ»ΡΡΠ°Π΅Ρ ΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅ ΡΡΠ°ΠΏΡ: Π·Π°Π±ΠΎΡ Π±ΠΈΠΎΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π°, Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΠ΅ Π½ΡΠΊΠ»Π΅ΠΈΠ½ΠΎΠ²ΡΡ ΠΊΠΈΡΠ»ΠΎΡ, ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΡ
Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊ, ΠΌΠ΅ΡΠΎΠ΄Ρ Π²Π°Π»ΠΈΠ΄Π°ΡΠΈΠΈ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π½ΡΠ΅ Π½Π° Π±Π°Π·Π΅ Π»Π°Π±ΠΎΡΠ°ΡΠΎΡΠΈΠΈ.
Π Π°ΠΊ ΠΏΠΈΡΠ΅Π²ΠΎΠ΄Π° Π·Π°Π½ΠΈΠΌΠ°Π΅Ρ ΡΠ΅ΡΡΠΎΠ΅ ΠΌΠ΅ΡΡΠΎ Π² ΡΡΡΡΠΊΡΡΡΠ΅ ΠΎΠ½ΠΊΠΎΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ Π² ΠΠ°Π·Π°Ρ ΡΡΠ°Π½Π΅, ΠΈ, ΠΊΠ°ΠΊ ΠΏΡΠ°Π²ΠΈΠ»ΠΎ,
Π½Π΅ ΠΎΠ±Π½Π°ΡΡΠΆΠΈΠ²Π°Π΅ΡΡΡ, ΠΏΠΎΠΊΠ° Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠ΅ Π½Π΅ ΠΏΡΠΎΠ³ΡΠ΅ΡΡΠΈΡΡΠ΅Ρ Π΄ΠΎ Π·Π°ΠΏΡΡΠ΅Π½ΡΡ ΡΡΠ°Π΄ΠΈΠΉ. ΠΠΎΠ»Π΅Π΅ 80%
ΡΠ»ΡΡΠ°Π΅Π² Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π΅ΠΌΠΎΡΡΠΈ ΠΈ ΡΠΌΠ΅ΡΡΠ½ΠΎΡΡΠΈ ΠΏΡΠΈΡ ΠΎΠ΄ΠΈΡΡΡ Π½Π° ΡΠ°Π·Π²ΠΈΠ²Π°ΡΡΠΈΠ΅ΡΡ ΡΡΡΠ°Π½Ρ ΠΈ ΡΡΡΠ°Π½Ρ
Π¦Π΅Π½ΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΈ ΠΠΎΡΡΠΎΡΠ½ΠΎΠΉ ΠΠ·ΠΈΠΈ. Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: Π²ΡΡΠ²Π»Π΅Π½ΠΈΠ΅ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΎΡΠ½ΠΎΠ² ΡΠ°ΠΊΠ°
ΠΏΠΈΡΠ΅Π²ΠΎΠ΄Π° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ»Π½ΠΎΠ³ΠΎ ΡΡΠ°Π½ΡΠΊΡΠΈΠΏΡΠΎΠΌΠ° Π² ΠΠ°Π·Π°Ρ ΡΡΠ°Π½Π΅.
Π Π΅ΠΊΡΡΡΠΈΠ½Π³ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡ Π½Π° Π±Π°Π·Π΅ ΠΎΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΡΠΎΡΠ°ΠΊΠ°Π»ΡΠ½ΠΎΠΉ Ρ ΠΈΡΡΡΠ³ΠΈΠΈ, ΠΎΠ½ΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ
ΡΠ΅Π½ΡΡΠ°, Π³. ΠΡΡΠ°Π½Π°. Π ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π±ΡΠ»ΠΈ Π²ΠΊΠ»ΡΡΠ΅Π½Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΡ Ρ: ΠΏΠΎΠ΄ΠΏΠΈΡΠ°Π½Π½ΡΠΌΠΈ ΡΠΎΡΠΌΠ°ΠΌΠΈ
ΠΈΠ½ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠΎΠ³Π»Π°ΡΠΈΡ, ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π΅Π½Π½ΡΠΌ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠΌ Π΄ΠΈΠ°Π³Π½ΠΎΠ·ΠΎΠΌ - ΠΏΠ»ΠΎΡΠΊΠΎΠΊΠ»Π΅ΡΠΎΡΠ½ΠΎΠ³ΠΎ
ΡΠ°ΠΊΠ° ΠΏΠΈΡΠ΅Π²ΠΎΠ΄Π°, ΠΊΠΎΡΠΎΡΡΠΌ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»Π°ΡΡ ΡΠ°Π΄ΠΈΠΊΠ°Π»ΡΠ½Π°Ρ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΡ (Ivor Lewis Esophagectomy), Ρ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ Π°Π½Π°Π»ΠΈΠ·Π° ΠΊΡΠΎΠ²ΠΈ, Π±ΠΈΠΎΡ ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ Π΄Π°Π½Π½ΡΡ , ΠΠ’, ΡΠ΅Π½ΡΠ³Π΅Π½-ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ,
Π³ΠΈΡΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌ Π·Π°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅ΠΌ.
ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ: ΠΏΠ°ΡΠ° ΡΠ²Π΅ΠΆΠ΅Π·Π°ΠΌΠΎΡΠΎΠΆΠ΅Π½Π½ΠΎΠΉ (Π»ΠΈΠ±ΠΎ ΠΎΠ±ΡΠ°Π·ΡΠ° ΡΠΊΠ°Π½Π΅ΠΉ Π² Π ΠΠ ΡΡΠ°Π±ΠΈΠ»ΠΈΠ·ΠΈΡΡΡΡΠ΅ΠΌ ΡΠ°ΡΡΠ²ΠΎΡΠ΅)
ΡΠΊΠ°Π½ΠΈ ΠΏΠΈΡΠ΅Π²ΠΎΠ΄Π° Ρ Π½ΠΎΡΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°ΡΡΠΊΠ° ΠΈ ΡΠ΅Π½ΡΡΠ° ΠΎΠΏΡΡ ΠΎΠ»ΠΈ.
Π ΡΠ°Π±ΠΎΡΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π»ΠΈΡΡ ΠΌΠ΅ΡΠΎΠ΄Ρ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΠΈ ΠΎΡΠΈΡΡΠΊΠΈ Π ΠΠ, ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠ° Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊ, ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅
ΠΌΠ΅ΡΠΎΠ΄Ρ Π²Π°Π»ΠΈΠ΄Π°ΡΠΈΠΈ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊ, Π³ΠΈΠ±ΡΠΈΠ΄ΠΈΠ·Π°ΡΠΈΡ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊ Π½Π° ΠΏΡΠΎΡΠΎΡΠ½ΡΡ ΡΡΠ΅ΠΉΠΊΡ, ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅
Π½Π° HiSeq 2000
ΠΠ»Ρ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΠΈ ΠΎΡΠΈΡΡΠΊΠΈ Π ΠΠ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈ Π½Π°Π±ΠΎΡΡ Qiagen, Π΄Π»Ρ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠΈ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠΈ
ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈ Tru Seq RNA sample preparation kit, Π²ΡΠ΅ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈΡΡ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ
ΠΏΡΠΎΡΠΎΠΊΠΎΠ»Π°ΠΌΠΈ Illumina.
Vladislav Govorovskiy
Illumina representative, Belarus
Application of Illumina NGS-technologies in healthcare, research and agriculture
Next-generation sequencing (NGS) technologies transform biological and medical research.
Researchers around the world use next-generation sequencing systems to drive genetic analysis at
higher rate.
Ongoing development of Sequence By Synthesis (SBS) technology provides possibilities to drive
various researches in spheres of interest: science, healthcare, agriculture, forensic, reproductive
medicine. Development of modern devices, such as MiniSeq, HiSeq 4000, HiSeq X, has provided
customers with new possibilities in the NGS sphere. Computational power of those machines coupled
with ongoing designing of kits and panels it has opened great prospects for modern research.
Illumina platform also allow using alternative methods of sample preparation that extends the
potential use of the system. Variety of discussed methods and their potential combinations provide
considerable scope expansion for modern science and medicine.
Π‘Π΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΈΠ»ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π² ΡΡΠ΅ΡΠ΅ Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Ρ.
ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΠΈ ΠΏΠΎ Π²ΡΠ΅ΠΌΡ ΠΌΠΈΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡ ΡΠΈΡΡΠ΅ΠΌΡ NGS Π΄Π»Ρ ΠΏΡΠΎΠ΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π°Π½Π°Π»ΠΈΠ·ΠΎΠ² Π΄ΠΎ ΡΠ°Π½Π΅Π΅ Π½Π΅Π΄ΠΎΡΡΠΈΠΆΠΈΠΌΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ.
ΠΠΎΡΡΠΎΡΠ½Π½ΠΎΠ΅ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ Sequence By Synthesis (SBS) ΠΎΡ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΈ Illumina
Π΄Π°ΡΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΡΡ Π²ΡΡ Π±ΠΎΠ»Π΅Π΅ ΡΠ°Π·Π½ΠΎΡΡΠΎΡΠΎΠ½Π½ΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π² ΡΠ°Π·Π½ΠΎΠΎΠ±ΡΠ°Π·Π½ΡΡ ΡΡΠ΅ΡΠ°Ρ
Π½Π°ΡΡΠ½ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, Π·Π΄ΡΠ°Π²ΠΎΠΎΡ ΡΠ°Π½Π΅Π½ΠΈΡ, ΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ Ρ ΠΎΠ·ΡΠΉΡΡΠ²Π°, ΠΊΡΠΈΠΌΠΈΠ½Π°Π»ΠΈΡΡΠΈΠΊΠΈ,
ΡΠ΅ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Ρ.
ΠΠΎΡΠ²Π»Π΅Π½ΠΈΠ΅ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΠΎΡΡΠ΄ΠΎΠ²Π°Π½ΠΈΡ, ΡΠ°ΠΊΠΎΠ³ΠΎ ΠΊΠ°ΠΊ MiniSeq, HiSeq 4000, HiSeq X, Π΄Π°Π»ΠΎ
ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΡΠΌ Π½ΠΎΠ²ΡΠ΅ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π² ΡΡΠ΅ΡΠ΅ NGS, Π° Π²ΠΌΠ΅ΡΡΠ΅ Ρ ΠΏΠΎΡΡΠΎΡΠ½Π½ΠΎ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΡΡΡΠΈΠΌΠΈΡΡ
Π½Π°Π±ΠΎΡΠ°ΠΌΠΈ ΠΈ ΠΏΠ°Π½Π΅Π»ΡΠΌΠΈ, ΡΡΠΎ ΠΎΡΠΊΡΡΠ»ΠΎ ΠΎΠ³ΡΠΎΠΌΠ½ΡΠ΅ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Ρ Π΄Π»Ρ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ.
ΠΠ»Π°ΡΡΠΎΡΠΌΠ° Illumina ΡΠ°ΠΊΠΆΠ΅ Π΄Π°Π΅Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ²Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ
ΠΏΡΠΎΠ±ΠΎΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠΈ, ΡΡΠΎ ΡΠ°ΡΡΠΈΡΡΠ΅Ρ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π» ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π΄Π°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ.
Π Π°Π·Π½ΠΎΠΎΠ±ΡΠ°Π·ΠΈΠ΅ ΠΈ ΠΊΠΎΠΌΠ±ΠΈΠ½Π°ΡΠΈΡ Π΄Π°Π½Π½ΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈ ΠΈΡ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΠ΅ ΠΊΠΎΠΌΠ±ΠΈΠ½Π°ΡΠΈΠΈ ΠΏΡΠ΅Π΄ΠΎΡΡΠ°Π²Π»ΡΡΡ
Π·Π½Π°ΡΠΈΠΌΠΎΠ΅ ΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΠ΅ Π³ΡΠ°Π½ΠΈΡ Π΄Π»Ρ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ Π½Π°ΡΠΊΠΈ ΠΈ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Ρ.
Askhat Molkenov
Nazarbayev University, Kazakhstan
Peculiarities of Bioinformatics Processing and Data Conversion from Illumina HiSeq2000
High throughput next generation sequencing platforms provided new opportunities to scientists in
genomic research field. Nowadays there are carried out large-scale genomic studies of different
organisms, including humans, animals, plants and bacteria with the usage of next generation
sequencing technologies. Modern bioinformatics is a synthesis of biological, information and technical
disciplines aimed to solve scientific problems. In this report, I will present some methods and examples
used in the daily analytical protocols on the base of Laboratory of Bioinformatics and Computational
Systems Biology for the analysis of genomic data from Illumina HiSeq 2000.
ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ Π±ΠΈΠΎΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π΄Π°Π½Π½ΡΡ Ρ ΠΏΠ»Π°ΡΡΠΎΡΠΌΡ HiSeq
2000.
ΠΡΡΠΎΠΊΠΎΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΠΏΠ»Π°ΡΡΠΎΡΠΌΡ ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ ΠΎΡΠΊΡΡΠ»ΠΈ ΠΏΠ΅ΡΠ΅Π΄
ΡΡΠ΅Π½ΡΠΌΠΈ Π½ΠΎΠ²ΡΠ΅ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π΄Π»Ρ Π³Π΅Π½ΠΎΠΌΠ½ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ. Π Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΡΡΡΡ
ΠΌΠ°ΡΡΡΠ°Π±Π½ΡΠ΅ Π³Π΅Π½ΠΎΠΌΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ ΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌΠΎΠ², Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π»ΡΠ΄Π΅ΠΉ, ΠΆΠΈΠ²ΠΎΡΠ½ΡΡ ,
ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ ΠΈ Π±Π°ΠΊΡΠ΅ΡΠΈΠΉ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ.
Π‘ΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ Π±ΠΈΠΎΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΊΠ° ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ ΡΠΎΠ±ΠΎΠΉ ΡΠΈΠ½ΡΠ΅Π· Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ , ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ ΠΈ
ΡΠ΅Ρ Π½ΠΈΡΠ΅ΡΠΊΠΈΡ Π΄ΠΈΡΡΠΈΠΏΠ»ΠΈΠ½, Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΡΡ Π½Π° ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ Π½Π°ΡΡΠ½ΡΡ Π·Π°Π΄Π°Ρ. Π ΡΠ²ΠΎΠ΅ΠΌ Π΄ΠΎΠΊΠ»Π°Π΄Π΅ Ρ
ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Ρ Π½Π΅ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΈ ΠΏΡΠΈΠΌΠ΅ΡΡ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΠ΅ Π² Π΅ΠΆΠ΅Π΄Π½Π΅Π²Π½ΡΡ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΎΡΠΎΠΊΠΎΠ»Π°Ρ Π½Π° Π±Π°Π·Π΅ ΠΠ°Π±ΠΎΡΠ°ΡΠΎΡΠΈΠΈ ΠΠΈΠΎΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΊΠΈ ΠΈ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΠ½ΠΎΠΉ Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΠΈ Π΄Π»Ρ
Π°Π½Π°Π»ΠΈΠ·Π° Π³Π΅Π½ΠΎΠΌΠ½ΡΡ Π΄Π°Π½Π½ΡΡ Ρ Illumina HiSeq2000.
Saule Daugalieva
Institute of Microbiology and Virology, Kazakhstan
NGS 16S sequencing for microbial identification
Laboratory shared of Institute Microbiology and Virology was established in 2014. In the laboratory,
performed the molecular genetic studies on research projects carried out in our institute. The
laboratory is equipped with modern equipment and everything necessary for the research to date. In
the laboratory there are: an Eppendorf PCR cycler, real-time PCR Applied Biosystems 7500, 8-capillary
sequencer Applied Biosystems 3500, next generation sequencer MiSeq Illumina. In addition, there are
accessories: spectrophotometer Quibit, Ajilent Bioanalyzer 2100 gel documentation system Vilber
Lourmat ECX- F15.M, and chromatography mass spectrometer Shimadzu LCMS-860.
Department of Microbiology of our Institute conducted Molecular following types studies of
microorganisms: oil degraded, cellulose degraded, nitrogen-fixing, lactic acid, plant pathogens,
bacteria, fungi and yeast. The main areas of research are the identification of microorganisms by PCR
analysis and sequencing, full genome analysis, and identification of specific genes. In the near future
we plan to hold the soil and water metagenomic analysis from different regions of Kazakhstan and of
the environment.
In 2014, we performed full genome analysis of 14 species of bacteria on the NGS-sequencer MiSeq
Illumina. At this sequencer we performed 16S metagenomic analysis of 120 strains of bacterial
cultures. Following the acquisition of capillary sequencer, we have conducted with the help of the
identification of 12 species of fungi, and 8 species of yeast, as well as 110 species of bacteria.
When conducting full genome analysis on MiSeq instrument we used a set of sample preparation and
Nextera XT kit for sequencing MiSeq Kit v2.
16S metagenomic analysis for libraries prepared using indexes Nextera XT Index Kit (24 Indexes, 96
Samples) Illumina using KAPA HIFI HOTSTART READY MIX. Purification was carried out using reagent
Ampure XP beads on the magnetic stand. Quantity and quality of the libraries was determined with a
spectrophotometer Quibit 2.0, and 2100 Bioanalyzer Ajilent 2100 and by horizontal gel
electrophoresis. These libraries were normalized and pooled. As a control, was added Phix Control v.3.
Sequencing was performed using a set MiSeq Kit v.2 (500 cycles) and MiSeq Kit v.3 (600 cycles). The
processing of the results was performed using MiSeq Reporter program and 16S metagenomic
program on Illumina website.
ΠΠ°Π±ΠΎΡΠ°ΡΠΎΡΠΈΡ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ½ΡΡΠΈΡΡΡΠ° ΠΌΠΈΠΊΡΠΎΠ±ΠΈΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈ Π²ΠΈΡΡΡΠΎΠ»ΠΎΠ³ΠΈΠΈ ΡΠΎΠ·Π΄Π°Π½Π° Π²
2014 Π³ΠΎΠ΄Ρ. Π Π»Π°Π±ΠΎΡΠ°ΡΠΎΡΠΈΠΈ Π²ΡΠΏΠΎΠ»Π½ΡΡΡΡΡ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΠΎ-Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎ Π½Π°ΡΡΠ½ΡΠΌ
ΠΏΡΠΎΠ΅ΠΊΡΠ°ΠΌ, ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠΌΡΠΌ Π² Π½Π°ΡΠ΅ΠΌ ΠΈΠ½ΡΡΠΈΡΡΡΠ΅. ΠΠ°Π±ΠΎΡΠ°ΡΠΎΡΠΈΡ ΠΎΡΠ½Π°ΡΠ΅Π½Π° ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠΌ
ΠΎΠ±ΠΎΡΡΠ΄ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΈ Π²ΡΠ΅ΠΌ Π½Π΅ΠΎΠ±Ρ ΠΎΠ΄ΠΈΠΌΡΠΌ Π΄Π»Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π½Π°ΡΡΠ½ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π½Π° ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΌ
ΡΡΠΎΠ²Π½Π΅. Π Π»Π°Π±ΠΎΡΠ°ΡΠΎΡΠΈΠΈ ΠΈΠΌΠ΅ΡΡΡΡ: ΠΠ¦Π -Π°ΠΌΠΏΠ»ΠΈΡΠΈΠΊΠ°ΡΠΎΡ Eppendorf, ΠΠ¦Π ΡΠ΅Π°Π»-ΡΠ°ΠΉΠΌ Applied
Biosystems 7500, 8-ΠΊΠ°ΠΏΠΈΠ»Π»ΡΡΠ½ΡΠΉ ΡΠ΅ΠΊΠ²Π΅Π½Π°ΡΠΎΡ Applied Biosystems 3500, ΡΠ΅ΠΊΠ²Π΅Π½Π°ΡΠΎΡ Π½ΠΎΠ²ΠΎΠ³ΠΎ
ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ MiSeq Illumina. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, ΠΈΠΌΠ΅Π΅ΡΡΡ Π²ΡΠΏΠΎΠΌΠΎΠ³Π°ΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΠΎΠ±ΠΎΡΡΠ΄ΠΎΠ²Π°Π½ΠΈΠ΅:
ΡΠΏΠ΅ΠΊΡΡΠΎΡΠΎΡΠΎΠΌΠ΅ΡΡ Quibit, Π±ΠΈΠΎΠ°Π½Π°Π»ΠΈΠ·Π°ΡΠΎΡ Ajilent 2100, ΡΠΈΡΡΠ΅ΠΌΠ° Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π³Π΅Π»Π΅ΠΉ Vilber
Lourmat ECX- F15.M, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΈΡΡΠ΅ΠΌΠ° Ρ ΡΠΎΠΌΠ°ΡΠΎΠΌΠ°ΡΡΠΏΠ΅ΠΊΡΡΠΎΠΌΠ΅ΡΡΠΈΠΈ Shimadzu LCMS-860.
ΠΡΠ΄Π΅Π»ΠΎΠΌ ΠΌΠΈΠΊΡΠΎΠ±ΠΈΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈΠ½ΡΡΠΈΡΡΡΠ° ΠΏΡΠΎΠ²ΠΎΠ΄ΡΡΡΡ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ»Π΅Π΄ΡΡΡΠΈΡ Π²ΠΈΠ΄ΠΎΠ²
ΠΌΠΈΠΊΡΠΎΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌΠΎΠ²: Π½Π΅ΡΡΠ΅ΠΎΠΊΠΈΡΠ»ΡΡΡΠΈΡ , ΡΠ΅Π»Π»ΡΠ»ΠΎΠ·ΠΎΠ»ΠΈΡΠ΅ΡΠ΅ΡΠΊΠΈΡ , Π°Π·ΠΎΡΡΠΈΠΊΡΠΈΡΡΡΡΠΈΡ ,
ΠΌΠΎΠ»ΠΎΡΠ½ΠΎΠΊΠΈΡΠ»ΡΡ , Π±Π°ΠΊΡΠ΅ΡΠΈΠΉ-ΡΠΈΡΠΎΠΏΠ°ΡΠΎΠ³Π΅Π½ΠΎΠ², Π³ΡΠΈΠ±ΠΎΠ² ΠΈ Π΄ΡΠΎΠΆΠΆΠ΅ΠΉ. ΠΡΠ½ΠΎΠ²Π½ΡΠΌΠΈ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΠΌΠΈ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΡΠ²Π»ΡΡΡΡΡ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΡΠ°ΠΌΠΌΠΎΠ² ΠΌΠΈΠΊΡΠΎΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΠΠ¦Π -Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ
ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΏΠΎΠ»Π½ΠΎΠ³Π΅Π½ΠΎΠΌΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ·, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΡ Π³Π΅Π½ΠΎΠ². Π
Π±Π»ΠΈΠΆΠ°ΠΉΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ ΠΌΡ ΠΏΠ»Π°Π½ΠΈΡΡΠ΅ΠΌ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΠ°Π³Π΅Π½ΠΎΠΌΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΏΠΎΡΠ²Ρ, Π²ΠΎΠ΄Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² ΠΠ°Π·Π°Ρ ΡΡΠ°Π½Π° ΠΈ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΎΠΊΡΡΠΆΠ°ΡΡΠ΅ΠΉ ΡΡΠ΅Π΄Ρ.
Π 2014 Π³ΠΎΠ΄Ρ Π½Π°ΠΌΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ ΠΏΠΎΠ»Π½ΠΎΠ³Π΅Π½ΠΎΠΌΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· 14 Π²ΠΈΠ΄ΠΎΠ² Π±Π°ΠΊΡΠ΅ΡΠΈΠΉ Π½Π° NGS-ΡΠ΅ΠΊΠ²Π΅Π½Π°ΡΠΎΡΠ΅
MiSeq Illumina. ΠΠ° Π΄Π°Π½Π½ΠΎΠΌ ΡΠ΅ΠΊΠ²Π΅Π½Π°ΡΠΎΡΠ΅ Π½Π°ΠΌΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ 16S ΠΌΠ΅ΡΠ°Π³Π΅Π½ΠΎΠΌΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΠΎΠΊΠΎΠ»ΠΎ 120
ΡΡΠ°ΠΌΠΌΠΎΠ² Π±Π°ΠΊΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΡ ΠΊΡΠ»ΡΡΡΡ. ΠΠΎΡΠ»Π΅ ΠΏΡΠΈΠΎΠ±ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΊΠ°ΠΏΠΈΠ»Π»ΡΡΠ½ΠΎΠ³ΠΎ ΡΠ΅ΠΊΠ²Π΅Π½Π°ΡΠΎΡΠ°, ΠΌΡ ΠΏΡΠΎΠ²Π΅Π»ΠΈ
Ρ Π΅Π³ΠΎ ΠΏΠΎΠΌΠΎΡΡΡ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ 12 Π²ΠΈΠ΄ΠΎΠ² Π³ΡΠΈΠ±ΠΎΠ² ΠΈ 8 Π²ΠΈΠ΄ΠΎΠ² Π΄ΡΠΎΠΆΠΆΠ΅ΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ 110 Π²ΠΈΠ΄ΠΎΠ²
Π±Π°ΠΊΡΠ΅ΡΠΈΠΉ.
ΠΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ ΠΏΠΎΠ»Π½ΠΎΠ³Π΅Π½ΠΎΠΌΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π½Π° ΠΏΡΠΈΠ±ΠΎΡΠ΅ MiSeq ΠΌΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈ Π½Π°Π±ΠΎΡ Π΄Π»Ρ
ΠΏΡΠΎΠ±ΠΎΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠΈ Nextera XT ΠΈ Π½Π°Π±ΠΎΡ Π΄Π»Ρ ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ MiSeq Kit v2.
ΠΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠΈ Π΄Π»Ρ 16S ΠΌΠ΅ΡΠ°Π³Π΅Π½ΠΎΠΌΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π³ΠΎΡΠΎΠ²ΠΈΠ»ΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΈΠ½Π΄Π΅ΠΊΡΠΎΠ² Nextera XT Index Kit
(24 Indexes, 96 Samples) Illumina Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ KAPA HIFI HOTSTART READY MIX. ΠΡΠΈΡΡΠΊΡ
ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠ΅Π°Π³Π΅Π½ΡΠ° Ampure XP beads Π½Π° ΠΌΠ°Π³Π½ΠΈΡΠ½ΠΎΠΌ ΡΡΠ°ΡΠΈΠ²Π΅. ΠΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²ΠΎ
Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ»ΠΈ Π½Π° ΡΠΏΠ΅ΠΊΡΡΠΎΡΠΎΡΠΎΠΌΠ΅ΡΡΠ΅ Quibit 2.0, Π±ΠΈΠΎΠ°Π½Π°Π»ΠΈΠ·Π°ΡΠΎΡΠ΅ Ajilent 2100 ΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ
Π³ΠΎΡΠΈΠ·ΠΎΠ½ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π³Π΅Π»Ρ-ΡΠ»Π΅ΠΊΡΡΠΎΡΠΎΡΠ΅Π·Π°. ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠΈ Π½ΠΎΡΠΌΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π»ΠΈ ΠΈ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½ΡΠ»ΠΈ.
Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ Π΄ΠΎΠ±Π°Π²Π»ΡΠ»ΠΈ Phix Control v.3. Π‘Π΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π½Π°Π±ΠΎΡΠ°
MiSeq Kit v.2 (500 ΡΠΈΠΊΠ»ΠΎΠ²) ΠΈ MiSeq Kit v.3 (600 ΡΠΈΠΊΠ»ΠΎΠ²). ΠΠ±ΡΠ°Π±ΠΎΡΠΊΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ²
ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ MiSeq Reporter ΠΈ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ 16S metagenomic Π½Π° ΡΠ°ΠΉΡΠ΅
Illumina.
Raushan Nugmanova
National Center for Biotechnology, Kazakhstan
Study of mutation clusters using Ion Torrent
Phenomenon of nonuniform pattern of mutations in the genome has been observed for many years.
Recent studies have shown presence of certain mutation clusters in cancer genomes, yeast cells, Big
Blue mice, retroelements as well as in bacteria under the pressure of the DNA damaging agents. Such
clusters were detected in the particular regions of the genome, accumulating within a number of
generations. Deep understanding of mutagenesis effect became possible with the development of
next generation sequencing, emergence of which provides deep and sensitive analysis of broad range
of mutations at high speed, generating high quality data. Therefore such approach is widely used in
genome-wide studies. The study of mutagenesis in bacteria is crucial as it might work as a potential
anti-bacterial treatment or may show new aspects of bacterial genome organization. As the previously
conducted study revealed presence of mutation clusters in the several E.coli genomes after the
mutagenesis by ethyl methanesulphonate (EMS), it is important to see whether this phenomenon is
unique only for Gram-negative E.coli, or also might be found in Gram-positive bacteria species as
B.subtilis after the EMS treatment. The use of Ion-Torrent Next-Generation Sequencing technology
allows analyzing several bacterial genomes in one run. The results of the current study showed
presence of mutation clusters in the genome of B.subtilis. In addition, further work is required to
understand molecular basics of mutation clusters in ΞAda and ΞMutS E.coli strains.
Π€Π΅Π½ΠΎΠΌΠ΅Π½ Π½Π΅ΡΠ°Π²Π½ΠΎΠΌΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΌΡΡΠ°ΡΠΈΠΉ Π² Π³Π΅Π½ΠΎΠΌΠ΅ Π½Π°Π±Π»ΡΠ΄Π°Π΅ΡΡΡ Π½Π° ΠΏΡΠΎΡΡΠΆΠ΅Π½ΠΈΠΈ ΠΌΠ½ΠΎΠ³ΠΈΡ
Π»Π΅Ρ. ΠΠ΅Π΄Π°Π²Π½ΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ Π½Π°Π»ΠΈΡΠΈΠ΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΡ ΠΊΠ»Π°ΡΡΠ΅ΡΠΎΠ² ΠΌΡΡΠ°ΡΠΈΠΉ Π² Π³Π΅Π½ΠΎΠΌΠ°Ρ
ΡΠ°ΠΊΠ°, Π΄ΡΠΎΠΆΠΆΠ°Ρ , Big Blue mouse, ΡΠ΅ΡΡΠΎΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°Ρ , Π° ΡΠ°ΠΊΠΆΠ΅ Π² Π±Π°ΠΊΡΠ΅ΡΠΈΡΡ ΠΏΠΎΠ΄ Π΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ΠΌ ΠΠΠ-
ΠΏΠΎΠ²ΡΠ΅ΠΆΠ΄Π°ΡΡΠΈΡ Π°Π³Π΅Π½ΡΠΎΠ². ΠΠΎΠ΄ΠΎΠ±Π½ΡΠ΅ ΠΊΠ»Π°ΡΡΠ΅ΡΡ Π±ΡΠ»ΠΈ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½Ρ Π² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΡ ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
Π³Π΅Π½ΠΎΠΌΠ°, Π½Π°ΠΊΠΎΠΏΠ»ΠΈΠ²Π°ΡΡΡ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΠΉ. ΠΠ»ΡΠ±ΠΎΠΊΠΎΠ΅ ΠΏΠΎΠ½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΡΡΠ΅ΠΊΡΠ°
ΠΌΡΡΠ°Π³Π΅Π½Π΅Π·Π° ΡΡΠ°Π»ΠΎ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΠΌ Ρ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ΠΌ ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ, ΠΏΠΎΡΠ²Π»Π΅Π½ΠΈΠ΅
ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°Π΅Ρ Π΄Π΅ΡΠ°Π»ΡΠ½ΡΠΉ ΠΈ ΡΠΎΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠΈΡΠΎΠΊΠΎΠ³ΠΎ ΡΠΏΠ΅ΠΊΡΡΠ° ΠΌΡΡΠ°ΡΠΈΠΉ Π½Π° Π²ΡΡΠΎΠΊΠΎΠΉ
ΡΠΊΠΎΡΠΎΡΡΠΈ, Π³Π΅Π½Π΅ΡΠΈΡΡΡ Π²ΡΡΠΎΠΊΠΎΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ Π΄Π°Π½Π½ΡΠ΅. ΠΠΎΡΡΠΎΠΌΡ ΠΏΠΎΠ΄ΠΎΠ±Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ ΡΠΈΡΠΎΠΊΠΎ
ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ Π² Π³Π΅Π½ΠΎΠΌΠ½ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡΡ . ΠΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΠΌΡΡΠ°Π³Π΅Π½Π΅Π·Π° Π² Π±Π°ΠΊΡΠ΅ΡΠΈΡΡ Π½Π΅ΠΎΠ±Ρ ΠΎΠ΄ΠΈΠΌΠΎ, ΡΠ°ΠΊ
ΠΊΠ°ΠΊ ΡΡΠΎ ΠΌΠΎΠΆΠ΅Ρ ΡΡΠ°ΡΡ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΠΌ Π°Π½ΡΠΈΠ±Π°ΠΊΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΠΌ Π»Π΅ΡΠ΅Π½ΠΈΠ΅ΠΌ ΠΈΠ»ΠΈ ΠΌΠΎΠΆΠ΅Ρ ΠΎΡΠΊΡΡΡΡ Π½ΠΎΠ²ΡΠ΅
Π°ΡΠΏΠ΅ΠΊΡΡ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ Π³Π΅Π½ΠΎΠΌΠ° Π±Π°ΠΊΡΠ΅ΡΠΈΠΉ. Π’Π°ΠΊ ΠΊΠ°ΠΊ ΠΏΡΠ΅Π΄ΡΠ΄ΡΡΠ΅Π΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΎ Π½Π°Π»ΠΈΡΠΈΠ΅
ΠΊΠ»Π°ΡΡΠ΅ΡΠΎΠ² Π² Π³Π΅Π½ΠΎΠΌΠ΅ ΠΊΠΈΡΠ΅ΡΠ½ΠΎΠΉ ΠΏΠ°Π»ΠΎΡΠΊΠΈ ΠΏΠΎΡΠ»Π΅ ΠΌΡΡΠ°Π³Π΅Π½Π΅Π·Π° ΡΡΠΈΠ»ΠΌΠ΅ΡΠ°Π½ΠΎΡΡΠ»ΡΡΠΎΠ½Π°ΡΠΎΠΌ, Π²Π°ΠΆΠ½ΠΎ
ΠΏΡΠΎΡΠ»Π΅Π΄ΠΈΡΡ ΡΠ²Π»ΡΠ΅ΡΡΡ Π»ΠΈ Π΄Π°Π½Π½ΡΠΉ ΡΠ΅Π½ΠΎΠΌΠ΅Π½ ΡΠ½ΠΈΠΊΠ°Π»ΡΠ½ΡΠΌ ΡΠΎΠ»ΡΠΊΠΎ Π΄Π»Ρ Π³ΡΠ°ΠΌΠΎΡΡΠΈΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠΉΠΎΠΉ E.coli
ΠΈΠ»ΠΈ ΠΆΠ΅ ΡΠ°ΠΊΠΆΠ΅ ΠΏΡΠΈΡΡΡΡΡΠ²ΡΠ΅Ρ Π² Π³ΡΠ°ΠΌΠΎΠΏΠΎΠ»ΠΎΠΆΠΈΡΠ΅Π»ΡΠ½ΠΎΠΌ B.subtilis. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ Ion-
Torrent Next-Generation Sequencing ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΎ Π³Π΅Π½ΠΎΠΌΠΎΠ² Π±Π°ΠΊΡΠ΅ΡΠΈΠΉ
Π·Π° ΠΎΠ΄ΠΈΠ½ ΠΏΡΠΎΠ±Π΅Π³. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ Π½Π°Π»ΠΈΡΠΈΠ΅ ΠΌΡΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ ΠΊΠ»Π°ΡΡΠ΅ΡΠΎΠ² Π²
Π³Π΅Π½ΠΎΠΌΠ΅ B.subtilis. Π ΡΠΎΠΌΡ ΠΆΠ΅ ΡΡΠ΅Π±ΡΠ΅ΡΡΡ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ°Ρ ΡΠ°Π±ΠΎΡΠ°, ΡΡΠΎΠ±Ρ ΠΏΠΎΠ½ΡΡΡ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΡΠ΅ ΠΎΡΠ½ΠΎΠ²Ρ
ΠΊΠ»Π°ΡΡΠ΅ΡΠΎΠ² ΠΌΡΡΠ°ΡΠΈΠΉ Π² ΡΡΠ°ΠΌΠΌΠ°Ρ ΞAda ΠΈ ΞMutS E.coli.
Alexander Shevtsov
National Center for Biotechnology, Kazakhstan
NGS sequencing of veterinary pathogens
The previous two decades have led to a reduction in saiga populations by 95%, which connected with
the uncontrolled providence in the period 1994-2003. Various measures helped to reverse the
situation, and in 2013 in Kazakhstan, the number of saiga population has increased 5 times and
amounted to 110 thousand. However, despite the growth of the saiga population in Kazakhstan, they
are still in danger of extinction from infectious diseases. The main cause of the mass death of saiga in
Kazakhstan was recognized as pasteurellosis, a zoonotic disease of vertebrate animals, which is the
etiological agent of P. multocida. Despite the high ecological damage done by pasteurellosis there is a
little information about the genetic factors of the high pathogenesis of causative agent selected from
the saiga. In this research there was carried out whole genome sequencing of three strains of P.
multocida. The strain of P. multocida Z-1 was isolated from the Ural population fallen during the
outbreak in 2010 which killed a third of the population (11,920 individuals). Strains of P. multocida Z-
3 and P. multocida K-1 isolated from Betpakdalasaiga populations during outbreaks of 2012 and 2013.
Whole genome sequencing with the using IonTorrent allowed to get 2,184,434 readings for strain P.
multocida Z-1, 2,212,653 readings for strain P. multocida Z-3, 1,893,014 readings for strain P.
multocida K-1, with an average length of about 160 bp reads . The collected genomes were as follows:
2288383, 2336270 and 2303903 bp respectively. Despite the fact that two saiga populations do not
cross in the wild, strains of P. multocida isolated from them have a large set of identical genes (2025),
which is comprised of 92.6%-95,8% of the predicted proteins, which exceeds the numerical value of
the major genes previously analyzed Pasteurella spp. Meanwhile the strains from bekpakdalasaiga
populations have the largest pool of common genes compared with the Ural population. A
comparative analysis of the genomes of strains isolated from Saiga 11 with the genomes of strains
isolated from mammals and 3 genomes of birds revealed the unique genes of strain Z1 (24 genes), Z3
(35 genes) and K1 (21 genes). Most of these genes are identical bacteriophages or were small
predicted proteins of unknown function (s1). 40 genes have been characterized for all three analyzed
strains.
ΠΡΠ΅Π΄ΡΠ΅ΡΡΠ²ΡΡΡΠΈΠ΅ Π΄Π²Π° Π΄Π΅ΡΡΡΠΈΠ»Π΅ΡΠΈΡ ΠΏΡΠΈΠ²Π΅Π»ΠΈ ΠΊ ΡΠΎΠΊΡΠ°ΡΠ΅Π½ΠΈΡ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΉ ΡΠ°ΠΉΠ³ΠΈ Π½Π° 95%, ΡΡΠΎ ΡΠ²ΡΠ·Π°Π½ΠΎ
Ρ Π½Π΅ΠΊΠΎΠ½ΡΡΠΎΠ»ΠΈΡΡΠ΅ΠΌΡΠΌ ΠΏΡΠΎΠΌΡΡΠ»ΠΎΠΌ Π² ΠΏΠ΅ΡΠΈΠΎΠ΄ 1994-2003 Π³ΠΎΠ΄ΠΎΠ². Π Π°Π·Π»ΠΈΡΠ½ΡΠ΅ ΠΌΠ΅ΡΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΈ
ΠΏΠ΅ΡΠ΅Π»ΠΎΠΌΠΈΡΡ ΡΠΈΡΡΠ°ΡΠΈΡ, ΠΈ ΡΠΆΠ΅ Π² 2013 Π³ΠΎΠ΄Ρ Π² ΠΠ°Π·Π°Ρ ΡΡΠ°Π½Π΅ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΡ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΡΠ°ΠΉΠ³Π°ΠΊΠΎΠ²
ΡΠ²Π΅Π»ΠΈΡΠΈΠ»Π°ΡΡ Π² 5 ΡΠ°Π· ΠΈ ΡΠΎΡΡΠ°Π²ΠΈΠ»Π° 110 ΡΡΡ. ΠΠ΄Π½Π°ΠΊΠΎ, Π½Π΅ΡΠΌΠΎΡΡΡ Π½Π° ΡΠΎΡΡ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΉ ΡΠ°ΠΉΠ³ΠΈ Π²
ΠΠ°Π·Π°Ρ ΡΡΠ°Π½Π΅, ΠΈΠΌ Π΄ΠΎ Π½Π°ΡΡΠΎΡΡΠ΅Π³ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΡΠ³ΡΠΎΠΆΠ°Π΅Ρ ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΡ ΠΈΡΡΠ΅Π·Π½ΠΎΠ²Π΅Π½ΠΈΡ ΠΎΡ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΎΠ½Π½ΡΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ. ΠΡΠ½ΠΎΠ²Π½ΠΎΠΉ ΠΏΡΠΈΡΠΈΠ½ΠΎΠΉ ΠΌΠ°ΡΡΠΎΠ²ΠΎΠΉ Π³ΠΈΠ±Π΅Π»ΠΈ ΡΠ°ΠΉΠ³Π°ΠΊΠΎΠ² Π² ΠΠ°Π·Π°Ρ ΡΡΠ°Π½Π΅ Π±ΡΠ» ΠΏΡΠΈΠ·Π½Π°Π½
ΠΏΠ°ΡΡΠ΅ΡΠ΅Π»Π»Π΅Π·, Π·ΠΎΠΎΠ½ΠΎΠ·Π½Π°Ρ Π±ΠΎΠ»Π΅Π·Π½Ρ ΠΏΠΎΠ·Π²ΠΎΠ½ΠΎΡΠ½ΡΡ ΠΆΠΈΠ²ΠΎΡΠ½ΡΡ , ΡΡΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌ Π°Π³Π΅Π½ΡΠΎΠΌ ΠΊΠΎΡΠΎΡΠΎΠΉ
ΡΠ²Π»ΡΠ΅ΡΡΡ P.multocida. ΠΠ΅ΡΠΌΠΎΡΡΡ Π½Π° Π²ΡΡΠΎΠΊΠΈΠΉ ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΡΡΠΎΠ½ ΠΎΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ ΠΌΠ°Π»ΠΎ
ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΎ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ ΡΠ°ΠΊΡΠΎΡΠΎΠ² Π²ΡΡΠΎΠΊΠΎΠ³ΠΎ ΠΏΠ°ΡΠΎΠ³Π΅Π½Π΅Π·Π° Π²ΠΎΠ·Π±ΡΠ΄ΠΈΡΠ΅Π»Ρ Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΠΎΠ³ΠΎ ΠΎΡ ΡΠ°ΠΉΠ³ΠΈ.
Π Π΄Π°Π½Π½ΠΎΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΈ Π±ΡΠ»ΠΎ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ ΠΏΠΎΠ»Π½ΠΎΠ³Π΅Π½ΠΎΠΌΠ½ΠΎΠ΅ ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠ΅Ρ ΡΡΠ°ΠΌΠΌΠΎΠ² P.
multocida. Π¨ΡΠ°ΠΌΠΌ P. multocida Z-1 Π±ΡΠ» ΠΈΠ·ΠΎΠ»ΠΈΡΠΎΠ²Π°Π½ ΠΎΡ ΡΠ°ΠΉΠ³Π°ΠΊΠ° ΡΡΠ°Π»ΡΡΠΊΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΠΏΠ°Π²ΡΠ΅Π³ΠΎ Π²
ΠΏΠ΅ΡΠΈΠΎΠ΄ Π²ΡΠΏΡΡΠΊΠΈ 2010 Π³. ΡΠ½Π΅ΡΡΠ΅ΠΉ ΡΡΠ΅ΡΡ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ (11920 ΠΎΡΠΎΠ±Π΅ΠΉ). Π¨ΡΠ°ΠΌΠΌΡ P. multocida Z-3 ΠΈ P.
multocida Π-1 ΠΈΠ·ΠΎΠ»ΠΈΡΠΎΠ²Π°Π½Ρ ΠΎΡ ΡΠ°ΠΉΠ³Π°ΠΊΠΎΠ² Π±Π΅ΡΠΏΠ°ΠΊΠ΄Π°Π»ΠΈΠ½ΡΠΊΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ Π² ΠΏΠ΅ΡΠΈΠΎΠ΄ Π²ΡΠΏΡΡΠ΅ΠΊ 2012 ΠΈ
2013 Π³ΠΎΠ΄ΠΎΠ².
ΠΠΎΠ»Π½ΠΎΠ³Π΅Π½ΠΎΠΌΠ½ΠΎΠ΅ ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ IonTorrent, ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ ΠΏΠΎΠ»ΡΡΠΈΡΡ 2,184,434
ΠΏΡΠΎΡΡΠ΅Π½ΠΈΠΉ Π΄Π»Ρ ΡΡΠ°ΠΌΠΌΠ° P. multocida Z-1, 2,212,653 ΠΏΡΠΎΡΡΠ΅Π½ΠΈΠΉ Π΄Π»Ρ ΡΡΠ°ΠΌΠΌΠ° P. multocida Z-3 ΠΈ
1,893,014 ΠΏΡΠΎΡΡΠ΅Π½ΠΈΠΉ Π΄Π»Ρ ΡΡΠ°ΠΌΠΌΠ° P. multocida K-1, ΡΠΎ ΡΡΠ΅Π΄Π½Π΅ΠΉ Π΄Π»ΠΈΠ½ΠΎΠΉ ΠΏΡΠΎΡΡΠ΅Π½ΠΈΠΉ ΠΎΠΊΠΎΠ»ΠΎ 160 ΠΏ.Π½.
Π‘ΠΎΠ±ΡΠ°Π½Π½ΡΠ΅ Π³Π΅Π½ΠΎΠΌΡ ΡΠΎΡΡΠ°Π²ΠΈΠ»ΠΈ: 2288383, 2336270 ΠΈ 2303903 ΠΏ.Π½.
ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ. ΠΠ΅ΡΠΌΠΎΡΡΡ Π½Π° ΡΠΎ ΡΡΠΎ Π΄Π²Π΅ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΡΠ°ΠΉΠ³ΠΈ Π² Π΅ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ ΡΡΠ»ΠΎΠ²ΠΈΡΡ Π½Π΅
ΠΏΠ΅ΡΠ΅ΡΠ΅ΠΊΠ°ΡΡΡΡ, ΡΡΠ°ΠΌΠΌΡ P. multocida Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΠ΅ ΠΎΡ Π½ΠΈΡ ΠΈΠΌΠ΅ΡΡ Π±ΠΎΠ»ΡΡΠΎΠΉ Π½Π°Π±ΠΎΡ ΠΈΠ΄Π΅Π½ΡΠΈΡΠ½ΡΡ
Π³Π΅Π½ΠΎΠ² (2025), ΠΊΠΎΡΠΎΡΡΠΉ ΡΠΎΡΡΠ°Π²ΠΈΠ» 92,6%-95,8% ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½Π½ΡΡ Π±Π΅Π»ΠΊΠΎΠ², ΡΡΠΎ ΠΏΡΠ΅Π²ΡΡΠ°Π΅Ρ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΠ΅
Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ Π³Π΅Π½ΠΎΠ² ΡΠ°Π½Π΅Π΅ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΡΡ ΠΏΠ°ΡΡΠ΅ΡΠ΅Π»Π». ΠΡΠΈ ΡΡΠΎΠΌ ΡΡΠ°ΠΌΠΌΡ ΠΎΡ ΡΠ°ΠΉΠ³Π°ΠΊΠΎΠ²
Π±Π΅ΠΊΠΏΠ°ΠΊΡΠ΄Π°Π»ΠΈΠ½ΡΠΊΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΠΈΠΌΠ΅ΡΡ Π½Π°ΠΈΠ±ΠΎΠ»ΡΡΠΈΠΉ ΠΏΡΠ» ΠΎΠ±ΡΠΈΡ Π³Π΅Π½ΠΎΠ², Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ ΡΡΠ°Π»ΡΡΠΊΠΎΠΉ
ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠ΅ΠΉ. Π‘ΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π³Π΅Π½ΠΎΠΌΠΎΠ² ΡΡΠ°ΠΌΠΌΠΎΠ² Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΡ ΠΎΡ ΡΠ°ΠΉΠ³ΠΈ 11 Ρ Π³Π΅Π½ΠΎΠΌΠ°ΠΌΠΈ
ΡΡΠ°ΠΌΠΌΠΎΠ² Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΡ ΠΎΡ ΠΌΠ»Π΅ΠΊΠΎΠΏΠΈΡΠ°ΡΡΠΈΡ ΠΈ 3 Π³Π΅Π½ΠΎΠΌΠ°ΠΌΠΈ ΠΏΡΠΈΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ» Π²ΡΡΠ²ΠΈΡΡ ΡΠ½ΠΈΠΊΠ°Π»ΡΠ½ΡΠ΅
Π³Π΅Π½Ρ Π΄Π»Ρ ΡΡΠ°ΠΌΠΌΠ° Z1 (24 Π³Π΅Π½Π°), Z3 (35 Π³Π΅Π½ΠΎΠ²) ΠΈ K1 (21 Π³Π΅Π½). ΠΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²ΠΎ ΠΈΠ· ΡΡΠΈΡ Π³Π΅Π½ΠΎΠ² ΠΈΠ΄Π΅Π½ΡΠΈΡΠ½Ρ
Π±Π°ΠΊΡΠ΅ΡΠΈΠΎΡΠ°Π³Π°ΠΌ ΠΈΠ»ΠΈ ΠΊΠΎΡΠΎΡΠΊΠΈΠΌΠΈ Π±Π΅Π»ΠΊΠ°ΠΌΠΈ Ρ Π½Π΅ΠΈΠ·Π²Π΅ΡΡΠ½ΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠ΅ΠΉ. Π‘ΠΎΡΠΎΠΊ Π³Π΅Π½ΠΎΠ² Π±ΡΠ»ΠΈ Ρ Π°ΡΠ°ΠΊΡΠ΅ΡΠ½Ρ
Π΄Π»Ρ Π²ΡΠ΅Ρ ΡΡΠ΅Ρ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΡΡ ΡΡΠ°ΠΌΠΌΠΎΠ².
Aizhan Turmagambetova
Institute of Microbiology and Virology, Kazakhstan
Detection of viruses in environmental samples using NGS
Diagnostics of viral infections is on the verge of creating of new theories, hypotheses and discoveries
with the advent of NGS. This is due to several reasons, the most important of which are a multiple
increase of data about the availability of viruses in the environment, including soil, water, feces, air,
etc., as well as the ability to analyze of viruses without their cultivation.
In our research, we studied the biodiversity of viruses in the water reservoirs of Almaty region.
Sequencing was carried out by a double-barrel shotgun method. In this case the useful information
could be obtained by paired-end sequencing of DNA fragment. These two sequences are oriented in
opposite directions and along of the length of the fragment can be separated from each other, and
also can be used for genome assembling using different software. In our research was used the HiSeq
sequencing system and Edena software. Total contigs were 447,000 with a length of 200 to 80,000 bp.
The Metavir2 program selected the 184,431 contigs and the 249,780 of which was identified as viral
gene sequences, and 157,000 of which are previously unknown viral sequences.
Bacteriophages, algae viruses and viruses of the protozoa were the 97% of total viruses of this water
sample. Other 3% included the viruses capable of causing of the disease of animals, higher plants and
humans. Among them: 2 families of retro-transcribing viruses (Retroviridae, Caulimoviridae), 2
families of single-stranded RNA viruses (ssRNA viruses), family of single-stranded DNA virus (ssDNA
viruses - Inoviridae), family of double-stranded RNA virus (dsRNA viruses - Endornaviridae) and 20
families of double-stranded DNA viruses (dsDNA viruses, among them Herpesviridae, etc) were
detected.
Thus, the NGS is opening a new era in the development of monitoring of viral infections that allows
take a different look at the ecology of viruses.
ΠΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ° Π²ΠΈΡΡΠΎΠ² Π² ΠΎΠ±ΡΠ°Π·ΡΠ°Ρ ΠΎΠΊΡΡΡΠΆΠ°ΡΡΠ΅ΠΉ ΡΡΠ΅Π΄Ρ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΌΠ°ΡΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΠ°ΡΠ°Π»Π»Π΅Π»ΡΠ½ΠΎΠ³ΠΎ
ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ
Π‘ ΠΏΠΎΡΠ²Π»Π΅Π½ΠΈΠ΅ΠΌ ΠΌΠ°ΡΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΠ°ΡΠ°Π»Π»Π΅Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ (NGS β next generation sequencing)
Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ° Π²ΠΈΡΡΡΠ½ΡΡ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΉ ΡΡΠΎΠΈΡ Π½Π° ΠΏΠΎΡΠΎΠ³Π΅ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ Π½ΠΎΠ²ΡΡ ΡΠ΅ΠΎΡΠΈΠΉ, Π³ΠΈΠΏΠΎΡΠ΅Π· ΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ
Π½ΠΎΠ²ΡΡ ΠΎΡΠΊΡΡΡΠΈΠΉ. ΠΡΠΎ ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»Π΅Π½ΠΎ ΡΡΠ΄ΠΎΠΌ ΠΏΡΠΈΡΠΈΠ½ ΠΎΡΠ½ΠΎΠ²Π½ΡΠΌΠΈ ΠΈΠ· ΠΊΠΎΡΠΎΡΡΡ ΡΠ²Π»ΡΡΡΡΡ ΠΌΠ½ΠΎΠ³ΠΎΠΊΡΠ°ΡΠ½ΠΎΠ΅
ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ Π΄Π°Π½Π½ΡΡ ΠΎ Π½Π°Π»ΠΈΡΠΈΠΈ Π²ΠΈΡΡΡΠΎΠ² Π² ΠΎΠΊΡΡΠΆΠ°ΡΡΠ΅ΠΉ ΡΡΠ΅Π΄Π΅, Π²ΠΊΠ»ΡΡΠ°Ρ ΠΏΠΎΡΠ²Ρ, Π²ΠΎΠ΄Ρ, ΡΠΊΡΠΊΡΠ΅ΠΌΠ΅Π½ΡΡ,
Π²ΠΎΠ·Π΄ΡΡ ΠΈ Ρ.Π΄., Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ Π½Π°Π»ΠΈΡΠΈΠ΅ Π²ΠΈΡΡΡΠΎΠ² Π±Π΅Π· ΠΈΡ ΠΏΡΠ΅Π΄Π²Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ
ΠΊΡΠ»ΡΡΠΈΠ²ΠΈΡΠΎΠ²Π°Π½ΠΈΡ.
Π Π½Π°ΡΠΈΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ Π±ΠΈΠΎΡΠ°Π·Π½ΠΎΠΎΠ±ΡΠ°Π·ΠΈΡ Π²ΠΈΡΡΡΠΎΠ² Π² Π²ΠΎΠ΄ΠΎΠ΅ΠΌΠ°Ρ
ΠΠ»ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ. Π‘Π΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ Π΄Π²ΡΡΡΠ²ΠΎΠ»ΡΠ½ΠΎΠ³ΠΎ Π΄ΡΠΎΠ±ΠΎΠ²ΠΈΠΊΠ°. Π ΡΡΠΎΠΌ
ΡΠ»ΡΡΠ°Π΅ ΠΏΠΎΠ»Π΅Π·Π½Π°Ρ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Π° ΠΏΡΠΈ ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΏΠ°ΡΠ½ΡΡ ΠΊΠΎΠ½ΡΠΎΠ²
ΡΡΠ°Π³ΠΌΠ΅Π½ΡΠ° ΠΠΠ. ΠΡΠΈ Π΄Π²Π΅ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Ρ Π² ΠΏΡΠΎΡΠΈΠ²ΠΎΠΏΠΎΠ»ΠΎΠΆΠ½ΡΡ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΡ
ΠΈ ΠΏΠΎ Π΄Π»ΠΈΠ½Π΅ ΡΡΠ°Π³ΠΌΠ΅Π½ΡΠ° ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΎΡΠ΄Π΅Π»ΡΠ½Ρ Π΄ΡΡΠ³ ΠΎΡ Π΄ΡΡΠ³Π°, Π½ΠΎ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ Π΄Π»Ρ
ΡΠ±ΠΎΡΠΊΠΈ Π³Π΅Π½ΠΎΠΌΠΎΠ² Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠ°Π·Π»ΠΈΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ. Π Π½Π°ΡΠΈΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡΡ
Π±ΡΠ» ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ ΡΠ΅ΠΊΠ²Π΅Π½Π°ΡΠΎΡ HiSeq ΠΈ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ° Edena.
ΠΡΠ»ΠΎ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΎ 447000 ΠΊΠΎΠ½ΡΠΈΠ³ΠΎΠ² Ρ Π΄Π»ΠΈΠ½ΠΎΠΉ ΠΎΡ 200 Π΄ΠΎ 80000 ΠΏΠ°Ρ ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΉ, ΠΈΠ· ΠΊΠΎΡΠΎΡΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ°
Metavir2 ΠΎΡΠΎΠ±ΡΠ°Π»Π° 184431, Π² ΠΊΠΎΡΠΎΡΡΡ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π»Π° ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ 249780 Π²ΠΈΡΡΡΠ½ΡΡ
Π³Π΅Π½Π°, ΠΏΡΠΈ ΡΡΠΎΠΌ 157000 ΠΈΠ· Π½ΠΈΡ , ΡΡΠΎ ΡΠ°Π½Π΅Π΅ Π½Π΅ΠΈΠ·Π²Π΅ΡΡΠ½ΡΠ΅ Π²ΠΈΡΡΡΠ½ΡΠ΅ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ.
97% Π²ΠΈΡΡΡΠΎΠ² Π΄Π°Π½Π½ΠΎΠ³ΠΎ Π²ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΡΠ° ΡΠΎΡΡΠ°Π²Π»ΡΠ»ΠΈ Π±Π°ΠΊΡΠ΅ΡΠΈΠΎΡΠ°Π³ΠΈ (5 ΡΠ΅ΠΌΠ΅ΠΉΡΡΠ²), Π²ΠΈΡΡΡΡ
Π²ΠΎΠ΄ΠΎΡΠΎΡΠ»Π΅ΠΉ (1 ΡΠ΅ΠΌΠ΅ΠΉΡΡΠ²ΠΎ) ΠΈ ΠΏΡΠΎΡΡΠ΅ΠΉΡΠΈΡ (1 ΡΠ΅ΠΌΠ΅ΠΉΡΡΠ²ΠΎ). ΠΡΡΠ°Π»ΡΠ½ΡΠ΅ 3% ΠΏΡΠΈΡΠ»ΠΈΡΡ Π½Π° Π²ΠΈΡΡΡΡ
ΡΠΏΠΎΡΠΎΠ±Π½ΡΠ΅ Π²ΡΠ·ΡΠ²Π°ΡΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ ΠΆΠΈΠ²ΠΎΡΠ½ΡΡ , Π²ΡΡΡΠΈΡ ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ ΠΈ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°. ΠΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΎ 2
ΡΠ΅ΠΌΠ΅ΠΉΡΡΠ²Π° ΡΠ΅ΡΡΠΎ ΡΡΠ°Π½ΡΠΊΡΠΈΠ±ΠΈΡΡΠ΅ΠΌΡΡ Π²ΠΈΡΡΡΠΎΠ² (Retroviridae, Caulimoviridae), 2 ΡΠ΅ΠΌΠ΅ΠΉΡΡΠ²Π° Π²ΠΈΡΡΡΠΎΠ²
Ρ ΠΎΠ΄Π½ΠΎΡΠ΅ΠΏΠΎΡΠ΅ΡΠ½ΠΎΠΉ Π ΠΠ (ssRNA viruses), 1 ΡΠ΅ΠΌΠ΅ΠΉΡΡΠ²ΠΎ Π²ΠΈΡΡΡΠΎΠ² Ρ ΠΎΠ΄Π½ΠΎΡΠ΅ΠΏΠΎΡΠ΅ΡΠ½ΠΎΠΉ ΠΠΠ (ssDNA
viruses - Inoviridae), 1 ΡΠ΅ΠΌΠ΅ΠΉΡΡΠ²ΠΎ Π²ΠΈΡΡΡΠΎΠ² Ρ Π΄Π²ΡΡΠ΅ΠΏΠΎΡΠ΅ΡΠ½ΠΎΠΉ Π ΠΠ (dsRNA viruses - Endornaviridae) ΠΈ
20 ΡΠ΅ΠΌΠ΅ΠΉΡΡΠ² Π²ΠΈΡΡΡΠΎΠ² Ρ Π΄Π²ΡΡΠ΅ΠΏΠΎΡΠ΅ΡΠ½ΠΎΠΉ ΠΠΠ (dsDNA viruses, ΡΡΠ΅Π΄ΠΈ ΠΊΠΎΡΠΎΡΡΡ ΡΠ°ΠΊΠΈΠ΅ ΠΊΠ°ΠΊ Herpesviridae
ΠΈ Ρ.Π΄.).
Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, NGS ΠΎΡΠΊΡΡΠ»ΠΎ Π½ΠΎΠ²ΡΠΉ ΡΡΠ°ΠΏ Π² ΡΠ°Π·Π²ΠΈΡΠΈΠΈ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° Π²ΠΈΡΡΡΠ½ΡΡ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΉ,
ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠΉ ΠΏΠΎ-Π΄ΡΡΠ³ΠΎΠΌΡ Π²Π·Π³Π»ΡΠ½ΡΡΡ Π½Π° ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡ Π²ΠΈΡΡΡΠΎΠ².
Kobey Karamendin
Institute of Microbiology and Virology, Kazakhstan
NGS 16S sequencing of necropsy material from Saiga antelope after a mass die-off in Spring 2015
During metagenomic studies using MiSeq sequencer to identify bacterial infections pathogens in Saiga
it was determined that 89.05% of all short reads were of bacteria of the genus Pasteurella, among
which the Pasteurella multocida species reached 48.32%. Other species were: Pasteurella eae - 10.75
%, Pasteurella pneumotropica - 4.06 %, Unclassified at Species level - 34.91 %.
ΠΡΠΈ ΠΌΠ΅ΡΠ°Π³Π΅Π½ΠΎΠΌΠ½ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡΡ Π½Π° ΡΠ΅ΠΊΠ²Π΅Π½Π°ΡΠΎΡΠ΅ MiSeq Π΄Π»Ρ Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ Π²ΠΎΠ·Π±ΡΠ΄ΠΈΡΠ΅Π»Π΅ΠΉ Π²ΡΠ΅Ρ
Π±Π°ΠΊΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΡ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΉ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΎ, ΡΡΠΎ 89.05 % Π²ΡΠ΅Ρ ΠΊΠΎΡΠΎΡΠΊΠΈΡ ΠΏΡΠΎΡΡΠ΅Π½ΠΈΠΉ ΡΠΎΡΡΠ°Π²Π»ΡΠ»ΠΈ
Π±Π°ΠΊΡΠ΅ΡΠΈΠΈ ΡΠΎΠ΄Π° Pasteurella, ΡΡΠ΅Π΄ΠΈ ΠΊΠΎΡΠΎΡΡΡ ΠΏΡΠ΅ΠΎΠ±Π»Π°Π΄Π°Π» Π²ΠΈΠ΄ Pasteurella multocida ΠΈ ΡΠΎΡΡΠ°Π²ΠΈΠ» 48,32
%. ΠΠ· Π΄ΡΡΠ³ΠΈΡ Π²ΠΈΠ΄ΠΎΠ² ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½Ρ: Pasteurella eae - 10.75 %, Pasteurella pneumotropica - 4.06 %,
Π½Π΅ΠΎΠΏΠΎΠ·Π½Π°Π½Π½ΡΠ΅ Π²ΠΈΠ΄Ρ - 34.91 %.
Participants
Name Area of research Country Institute
Abdikerim, Saltanat Molecular genetics KZ IGGC
Akhmetova, Ainur Genetics of Human Diseases KZ NU
Akilzhanova, Ainur Genomic and Personalized medicine KZ NU
Alexyuk, Madina antiviral protection research, metagenomics KZ IMV
Amirbekov, Aday Immunogenetic aspects of cancer screening KZ MU
Amirgazin, Asylulan Bacterial genomics KZ NCB
Bogoyavlenskiy, Andrey antiviral protection research, metagenomics
KZ IMV
Daugaliyeva, Saule Microbiology, metagenomics KZ IMV
Jantayeva, Kira Population genetics KZ MU
Jarmukhanov, Zharkyn Human genetics KZ NCB
Kachieva, Zulfiya Human diseases KZ MU
Kahbatkyzy, Nurzhibek Population genetics KZ IGGC
Kairov, Ulykbek Bioinformatics & KZ NU
Kamalova, Dinara Bacterial genomics KZ NCB
Karamendin, Kobey Viral ecology, evolution KZ IMV
Kozhamkulov, Ulan Microbiology, molecular epidemiology KZ NU
Kulnazarov, Batyr Microbiology, metagenomics KZ IMV
Kuzovleva, Elena Population genetics KZ IGGC
Kydyrmanov, Aidyn Viral ecology, evolution KZ IMV
Moldakozhayev, Alibek Viral ecology, evolution KZ IMV
Nugmanova, Raushan Bacterial genomics KZ NCB
Nurmoldin, Shalkar Thyroid cancer research KZ MU
Perfilyeva, Anastasiya Molecular genetics KZ IGGC
Rakhimova, Saule Genetic studies of multifactorial diseases KZ NU
Shevtsov, Alexandr Bacterial genomics KZ NCB
Turmagambetova, Aizhan antiviral protection research, metagenomics
KZ IMV
Zholdybayeva, Elena Viral genetics KZ NCB
Zhunussova, Gulnur Molecular genetics KZ IGGC
Torokeldiev, Nurlan Population genetics KRG IAUB
Zhanibek Egizbayev Illumina representative KZ ILLM
Govorovskiy, Vladislav Illumina representative BLR ILLM
Carr, Ian Bioinformatics & health UK UoL
Dawson, Deborah Population & ecological genetics UK UoSh
Duncan, Elizabeth Genomics & evolutionary biology UK UoL
Dunn, Jennifer Disease ecology, conservation UK RSPB
Ford, Antonia Population & ecological genetics, genomics UK UoB
Forde, Niamh Reproductive biology UK UoL
Goodman, Simon Population genetics, disease ecology, conservation UK UoL
Hipperson, Helen Bioinformatics & population genetics UK UoSh
Knight, Christopher Microbial systems biology UK UoM
O'Connell, Mary Computational biology UK UoL
Stockdale, Jennifer Disease ecology, conservation UK UoC
Taylor, Morag Cancer genetics UK UoL
KZ β Kazakhstan
UK β United kingdom
KRG β Kyrgyzstan
BLR - Belarus
UoL - University of Leeds
UoM - University of Manchester
UoSh - University of Sheffield
UoC - University of Cardiff
UoB - University of Bangor
RSPB - Royal Society for Protection of Birds
ILLM β Illumina Corp. IMV - Institute of Microbiology and Virology
MU β Medical University
NCB - National Center for Biotechnology
IGGC β Institute of General Genetics and Cytology
NU - Nazarbayev University
IAUB - International Ala-Too University in Bishkek