Анализ данных секвенирования 2

2026/2027

Статус: Маго-лего

Кто читает: Департамент больших данных и информационного поиска

Где читается: Факультет компьютерных наук

Охват аудитории: для всех кампусов НИУ ВШЭ

Язык: русский

Кредиты: 6

Контактные часы: 56

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Аннотация

The discipline "Bioinformatics for Next Generation Sequencing" is taught in order to provide students with a comprehensive understanding of modern methods of mass parallel sequencing (NGS) data analysis. The course covers key areas of NGS application: transcriptomics (RNA-seq, scRNA-seq), genomics, metagenomics, epigenomics (ChIP-seq, ATAC-seq, Hi-C) and the study of RNA-chromatin interactions. Students will acquire practical skills in working with raw data (fastq), quality control, alignment, variant interpretation (vcf), gene expression analysis, metagenomic data processing, and working with NGS data presentation formats (BAM, SAM, VCF). Special attention is paid to the development of modern bioinformatic pipelines and tools (GATK, CellRanger, Kallisto, QIIME2), statistical analysis methods and the basics of machine learning for bioinformatics tasks.

Цель освоения дисциплины

Formation of students' systemic knowledge and practical skills in the field of bioinformatic analysis of next-generation sequencing data, including raw data processing, statistical analysis, interpretation of results, and solving applied research problems in genomics and transcriptomics.

Планируемые результаты обучения

Master the standard pipeline for analyzing genomic variants.
Master the skills of working with basic bioinformatic pipelines (GATK, RNA-seq analysis, ChIP-seq analysis).
Be able to interpret the results of variant analysis (VCF), differential expression, and ChIP-seq/ATAC-seq peaks.
Know how the main NGS platforms (Illumina, PacBio, Oxford Nanopore) and data formats work.
Get hands-on experience working with metagenomic data.
Be able to evaluate the quality of sequencing, perform preprocessing (trimming, filtering) of data.
Understand and apply machine learning techniques to reduce dimensionality and analyze high-dimensional NGS data.
Develop epigenomics data analysis skills.
Know the methods of single-cell RNA-seq data analysis and metagenomics (16S, WGS).
Master the basic principles of scRNA-seq data analysis.
To develop the skills of primary RNA-seq data analysis.

Содержание учебной дисциплины

Experimental NGS methods and primary data analysis
Transcriptomics (RNA-seq)
Single-cell RNA-seq (scRNA-seq)
Machine learning for NGS data
Epigenomics: Hi-C
Metagenomics
Genomics and Medical applications
Epigenomics: ChIP-seq and ATAC-seq
The RNA-chromatin interactome

Элементы контроля

Домашняя работа 1 (HW1)
RNA-seq data analysis: quality control of FASTQ files, alignment to the reference genome, quantification of genes, basic analysis of differential expression.
Домашняя работа 2 (HW2)
Single-cell RNA-seq data analysis: filtering, normalization, dimensionalization, clusterization, and cluster annotation.
Домашняя работа 3 (HW3)
Analysis of metagenomics (16S) data in QIIME2: denoising, construction of a phylogenetic tree, calculation of alpha and beta diversity.
Домашняя работа 4 (HW4)
ChIP-seq/ATAC-seq data analysis: quality control, alignment, peak calling, peak annotation, and motive analysis.
Домашняя работа 5 (HW5)
Calling genomic variants using the GATK pipeline: BQSR, HaplotypeCaller, filtering and annotation of the VCF file.
Экзамен
A theoretical exam covering all the topics of the course.

Промежуточная аттестация

2026/2027 2nd module
0.12 * Домашняя работа 2 (HW2) + 0.12 * Домашняя работа 1 (HW1) + 0.4 * Экзамен + 0.09 * Домашняя работа 4 (HW4) + 0.09 * Домашняя работа 5 (HW5) + 0.18 * Домашняя работа 3 (HW3)

Список литературы

Авторы

Емашева Валерия Анатольевна

Программа дисциплины