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Institution

Saint Petersburg Academic University

EducationSaint Petersburg, Russia
About: Saint Petersburg Academic University is a education organization based out in Saint Petersburg, Russia. It is known for research contribution in the topics: Nanowire & Quantum dot. The organization has 569 authors who have published 1093 publications receiving 32015 citations. The organization is also known as: St Petersburg Academic University — Nanotechnology Research and Education Centre of the Russian Academy of Sciences & Saint Petersburg Academic University.


Papers
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Journal ArticleDOI
TL;DR: SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies.
Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V−SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online (http://bioinf.spbau.ru/spades). It is distributed as open source software.

16,859 citations

Journal ArticleDOI
01 Jan 2014-Nature
TL;DR: In this paper, the authors report molecular profiling of 230 resected lung adnocarcinomas using messenger RNA, microRNA and DNA sequencing integrated with copy number, methylation and proteomic analyses.
Abstract: Adenocarcinoma of the lung is the leading cause of cancer death worldwide. Here we report molecular profiling of 230 resected lung adenocarcinomas using messenger RNA, microRNA and DNA sequencing integrated with copy number, methylation and proteomic analyses. High rates of somatic mutation were seen (mean 8.9 mutations per megabase). Eighteen genes were statistically significantly mutated, including RIT1 activating mutations and newly described loss-of-function MGA mutations which are mutually exclusive with focal MYC amplification. EGFR mutations were more frequent in female patients, whereas mutations in RBM10 were more common in males. Aberrations in NF1, MET, ERBB2 and RIT1 occurred in 13% of cases and were enriched in samples otherwise lacking an activated oncogene, suggesting a driver role for these events in certain tumours. DNA and mRNA sequence from the same tumour highlighted splicing alterations driven by somatic genomic changes, including exon 14 skipping in MET mRNA in 4% of cases. MAPK and PI(3)K pathway activity, when measured at the protein level, was explained by known mutations in only a fraction of cases, suggesting additional, unexplained mechanisms of pathway activation. These data establish a foundation for classification and further investigations of lung adenocarcinoma molecular pathogenesis.

4,104 citations

Journal ArticleDOI
Gad Getz1, Stacey Gabriel1, Kristian Cibulskis1, Eric S. Lander1  +280 moreInstitutions (31)
02 May 2013-Nature
TL;DR: In this paper, the authors performed an integrated genomic, transcriptomic and proteomic characterization of 373 endometrial carcinomas using array-and-sequencing-based technologies, and classified them into four categories: POLE ultramutated, microsatellite instability hypermutated, copy-number low, and copy number high.
Abstract: We performed an integrated genomic, transcriptomic and proteomic characterization of 373 endometrial carcinomas using array- and sequencing-based technologies. Uterine serous tumours and ∼25% of high-grade endometrioid tumours had extensive copy number alterations, few DNA methylation changes, low oestrogen receptor/progesterone receptor levels, and frequent TP53 mutations. Most endometrioid tumours had few copy number alterations or TP53 mutations, but frequent mutations in PTEN, CTNNB1, PIK3CA, ARID1A and KRAS and novel mutations in the SWI/SNF chromatin remodelling complex gene ARID5B. A subset of endometrioid tumours that we identified had a markedly increased transversion mutation frequency and newly identified hotspot mutations in POLE. Our results classified endometrial cancers into four categories: POLE ultramutated, microsatellite instability hypermutated, copy-number low, and copy-number high. Uterine serous carcinomas share genomic features with ovarian serous and basal-like breast carcinomas. We demonstrated that the genomic features of endometrial carcinomas permit a reclassification that may affect post-surgical adjuvant treatment for women with aggressive tumours.

3,719 citations

Journal ArticleDOI
TL;DR: Applications of the single-cell assembler SPAdes to a new approach for capturing and sequencing "microbial dark matter" that forms small pools of randomly selected single cells and further sequences all genomes from the mini-metagenome at once.
Abstract: Recent advances in single-cell genomics provide an alternative to largely gene-centric metagenomics studies, enabling whole-genome sequencing of uncultivated bacteria. However, single-cell assembly projects are challenging due to (i) the highly nonuniform read coverage and (ii) a greatly elevated number of chimeric reads and read pairs. While recently developed single-cell assemblers have addressed the former challenge, methods for assembling highly chimeric reads remain poorly explored. We present algorithms for identifying chimeric edges and resolving complex bulges in de Bruijn graphs, which significantly improve single-cell assemblies. We further describe applications of the single-cell assembler SPAdes to a new approach for capturing and sequencing “microbial dark matter” that forms small pools of randomly selected single cells (called a mini-metagenome) and further sequences all genomes from the mini-metagenome at once. On single-cell bacterial datasets, SPAdes improves on the recently deve...

1,067 citations

Book ChapterDOI
07 Apr 2013
TL;DR: Applications of the single-cell assembler SPAdes to a new approach for capturing and sequencing "dark matter of life" that forms small pools of randomly selected single cells and further sequences all genomes from the mini-metagenome at once.
Abstract: Recent advances in single-cell genomics provide an alternative to gene-centric metagenomics studies, enabling whole genome sequencing of uncultivated bacteria. However, single-cell assembly projects are challenging due to (i) the highly non-uniform read coverage, and (ii) a greatly elevated number of chimeric reads and read pairs. While recently developed single-cell assemblers have addressed the former challenge, methods for assembling highly chimeric reads remain poorly explored. We present algorithms for identifying chimeric edges and resolving complex bulges in de Bruijn graphs, which significantly improve single-cell assemblies. We further describe applications of the single-cell assembler SPAdes to a new approach for capturing and sequencing "dark matter of life" that forms small pools of randomly selected single cells (called a mini-metagenome) and further sequences all genomes from the mini-metagenome at once. We demonstrate that SPAdes enables sequencing mini-metagenomes and benchmark it against various assemblers. On single-cell bacterial datasets, SPAdes improves on the recently developed E+V-SC and IDBA-UD assemblers specifically designed for single-cell sequencing. For standard (multicell) datasets, SPAdes also improves on A5, ABySS, CLC, EULER-SR, Ray, SOAPdenovo, and Velvet.

595 citations


Authors

Showing all 575 results

NameH-indexPapersCitations
Pavel A. Pevzner9835260877
Alla Lapidus7739629182
Valeria Nikolaenko6019810661
V. M. Ustinov5257210645
Andrey Bogdanov5138411866
Ivan A. Shelykh453557299
A. E. Zhukov433745641
Vladimir G. Dubrovskii432966265
Nikolai N. Ledentsov4228310676
Mikhail V. Maximov393606735
G. E. Cirlin373264967
Alexey A. Bogdanov341793284
Ivan Iorsh342194012
Dmitrii Bogdanov30692815
Yu. M. Shernyakov291633027
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
20226
202134
202088
2019109
2018151