Computational analysis of bacterial RNA-Seq data
Ryan McClure,Divya Balasubramanian,Yan Sun,Maksym Bobrovskyy,Paul Sumby,Caroline A. Genco,Carin K. Vanderpool,Brian Tjaden +7 more
TLDR
The results suggest that Rockhopper can be used for efficient and accurate analysis of bacterial RNA-seq data, and that it can aid with elucidation of bacterial transcriptomes.Abstract:
Recent advances in high-throughput RNA sequencing (RNA-seq) have enabled tremendous leaps forward in our understanding of bacterial transcriptomes. However, computational methods for analysis of bacterial transcriptome data have not kept pace with the large and growing data sets generated by RNA-seq technology. Here, we present new algorithms, specific to bacterial gene structures and transcriptomes, for analysis of RNA-seq data. The algorithms are implemented in an open source software system called Rockhopper that supports various stages of bacterial RNA-seq data analysis, including aligning sequencing reads to a genome, constructing transcriptome maps, quantifying transcript abundance, testing for differential gene expression, determining operon structures and visualizing results. We demonstrate the performance of Rockhopper using 2.1 billion sequenced reads from 75 RNA-seq experiments conducted with Escherichia coli, Neisseria gonorrhoeae, Salmonella enterica, Streptococcus pyogenes and Xenorhabdus nematophila. We find that the transcriptome maps generated by our algorithms are highly accurate when compared with focused experimental data from E. coli and N. gonorrhoeae, and we validate our system's ability to identify novel small RNAs, operons and transcription start sites. Our results suggest that Rockhopper can be used for efficient and accurate analysis of bacterial RNA-seq data, and that it can aid with elucidation of bacterial transcriptomes.read more
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Integrative Genomics Viewer
James T. Robinson,Helga Thorvaldsdottir,Wendy Winckler,Mitchell Guttman,Eric S. Lander,Eric S. Lander,Gad Getz,Jill P. Mesirov +7 more
TL;DR: The sheer volume and scope of data posed by this flood of data pose a significant challenge to the development of efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data.
Journal ArticleDOI
De novo assembly of bacterial transcriptomes from RNA-seq data
TL;DR: This work presents novel algorithms, specific to bacterial gene structures and transcriptomes, for analysis of bacterial RNA-seq data and de novo transcriptome assembly, implemented in an open source software system called Rockhopper 2.
Journal ArticleDOI
Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center
Alice R. Wattam,James J. Davis,James J. Davis,Rida Assaf,Sébastien Boisvert,Thomas Brettin,Thomas Brettin,Christopher Bun,Neal Conrad,Neal Conrad,Emily M. Dietrich,Emily M. Dietrich,Terry Disz,Joseph L. Gabbard,Svetlana Gerdes,Christopher S. Henry,Ronald W. Kenyon,Dustin Machi,Chunhong Mao,Eric K. Nordberg,Gary J. Olsen,Daniel E. Murphy-Olson,Robert Olson,Robert Olson,Ross Overbeek,Bruce Parrello,Gordon D. Pusch,Maulik Shukla,Maulik Shukla,Veronika Vonstein,Andrew S. Warren,Fangfang Xia,Fangfang Xia,Hyunseung Yoo,Hyunseung Yoo,Rick Stevens,Rick Stevens +36 more
TL;DR: The Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center, which provides services for RNA-Seq, variation, model reconstruction and differential expression analysis, all delivered through an updated private workspace.
Journal ArticleDOI
Gut microbiota utilize immunoglobulin A for mucosal colonization
Gregory P. Donaldson,Mark S. Ladinsky,Kristie B. Yu,Jon G. Sanders,Bryan B. Yoo,Wen-Chi Chou,Margaret E. Conner,Ashlee M. Earl,Rob Knight,Pamela J. Bjorkman,Sarkis K. Mazmanian +10 more
TL;DR: It is proposed that IgA responses can be co-opted by the microbiome to engender robust host-microbial symbiosis and mediates stable colonization of the gut through exclusion of exogenous competitors.
Journal ArticleDOI
Microbial genomic analysis reveals the essential role of inflammation in bacteria-induced colorectal cancer
Janelle C. Arthur,Raad Z. Gharaibeh,Marcus Mühlbauer,Ernesto Perez-Chanona,Joshua M. Uronis,Jonathan McCafferty,Anthony A. Fodor,Christian Jobin +7 more
TL;DR: In this paper, the authors used 16S rRNA sequencing of luminal microbiota from ex-germ-free mice to show that inflamed mice maintain a higher abundance of Enterobacteriaceae than healthy wild-type mice.
References
More filters
Journal ArticleDOI
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini,Yosef Hochberg +1 more
TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Journal ArticleDOI
Fast and accurate short read alignment with Burrows–Wheeler transform
Heng Li,Richard Durbin +1 more
TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.
Journal ArticleDOI
Fast gapped-read alignment with Bowtie 2
TL;DR: Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
Journal ArticleDOI
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome
TL;DR: Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches and can be used simultaneously to achieve even greater alignment speeds.
Journal ArticleDOI
Differential expression analysis for sequence count data.
Simon Anders,Wolfgang Huber +1 more
TL;DR: A method based on the negative binomial distribution, with variance and mean linked by local regression, is proposed and an implementation, DESeq, as an R/Bioconductor package is presented.