Showing papers by "Zhong Wang published in 2015"

MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities

Abstract: Grouping large genomic fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Because of the complex nature of these communities, existing metagenome binning methods often miss a large number of microbial species. In addition, most of the tools are not scalable to large datasets. Here we introduce automated software called MetaBAT that integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency for accurate metagenome binning. MetaBAT outperforms alternative methods in accuracy and computational efficiency on both synthetic and real metagenome datasets. It automatically forms hundreds of high quality genome bins on a very large assembly consisting millions of contigs in a matter of hours on a single node. MetaBAT is open source software and available at https://bitbucket.org/berkeleylab/metabat.

Widespread Polycistronic Transcripts in Fungi Revealed by Single-Molecule mRNA Sequencing.

Abstract: Genes in prokaryotic genomes are often arranged into clusters and co-transcribed into polycistronic RNAs. Isolated examples of polycistronic RNAs were also reported in some higher eukaryotes but their presence was generally considered rare. Here we developed a long-read sequencing strategy to identify polycistronic transcripts in several mushroom forming fungal species including Plicaturopsis crispa, Phanerochaete chrysosporium, Trametes versicolor, and Gloeophyllum trabeum. We found genome-wide prevalence of polycistronic transcription in these Agaricomycetes, involving up to 8% of the transcribed genes. Unlike polycistronic mRNAs in prokaryotes, these co-transcribed genes are also independently transcribed. We show that polycistronic transcription may interfere with expression of the downstream tandem gene. Further comparative genomic analysis indicates that polycistronic transcription is conserved among a wide range of mushroom forming fungi. In summary, our study revealed, for the first time, the genome prevalence of polycistronic transcription in a phylogenetic range of higher fungi. Furthermore, we systematically show that our long-read sequencing approach and combined bioinformatics pipeline is a generic powerful tool for precise characterization of complex transcriptomes that enables identification of mRNA isoforms not recovered via short-read assembly.

A near complete snapshot of the Zea mays seedling transcriptome revealed from ultra-deep sequencing.

Abstract: RNA-sequencing (RNA-seq) enables in-depth exploration of transcriptomes, but typical sequencing depth often limits its comprehensiveness. In this study, we generated nearly 3 billion RNA-Seq reads, totaling 341 Gb of sequence, from a Zea mays seedling sample. At this depth, a near complete snapshot of the transcriptome was observed consisting of over 90% of the annotated transcripts, including lowly expressed transcription factors. A novel hybrid strategy combining de novo and reference-based assemblies yielded a transcriptome consisting of 126,708 transcripts with 88% of expressed known genes assembled to full-length. We improved current annotations by adding 4,842 previously unannotated transcript variants and many new features, including 212 maize transcripts, 201 genes, 10 genes with undocumented potential roles in seedlings as well as maize lineage specific gene fusion events. We demonstrated the power of deep sequencing for large transcriptome studies by generating a high quality transcriptome, which provides a rich resource for the research community.

Performance evaluation and tuning of BioPig for genomic analysis

Abstract: In this study, we aim to optimize Hadoop parameters to improve the performance of BioPig on Amazon Web Service (AWS). BioPig is a toolkit for large-scale sequencing data analysis and is built on Hadoop and Pig that enables easy parallel programming and scaling to datasets of terabyte sizes. AWS is the most popular cloud-computing platform offered by Amazon. When running BioPig jobs on AWS, the default configuration parameters may lead to high computational costs. We select the k-mer counting as it is used in a large number of next generation sequence (NGS) data analysis tools. We tuned Hadoop parameters from five different perspectives based on a baseline configuration. We found tuning different Hadoop parameters led to various performance improvements. The overall job execution time of k-mer counting on BioPig was reduced by 50% using an optimized set of parameters. This paper documents our tuning experiments as a valuable reference for future Hadoop-based analytics applications on genomics datasets.