TransLiG: a de novo transcriptome assembler that uses line graph iteration.
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ABSTRACT:
SUBMITTER: Liu J
PROVIDER: S-EPMC6480747 | biostudies-literature | 2019 Apr
REPOSITORIES: biostudies-literature
Publications
TransLiG: a de novo transcriptome assembler that uses line graph iteration.
Liu Juntao J Yu Ting T Mu Zengchao Z Li Guojun G
Genome biology 20190423 1
We present TransLiG, a new de novo transcriptome assembler, which is able to integrate the sequence depth and pair-end information into the assembling procedure by phasing paths and iteratively constructing line graphs starting from splicing graphs. TransLiG is shown to be significantly superior to all the salient de novo assemblers in both accuracy and computing resources when tested on artificial and real RNA-seq data. TransLiG is freely available at https://sourceforge.net/projects/transcript ...[more]
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Project description:Characterizing transcriptomes in non-model organisms has resulted in a massive increase in our understanding of biological phenomena. This boon, largely made possible via high-throughput sequencing, means that studies of functional, evolutionary, and population genomics are now being done by hundreds or even thousands of labs around the world. For many, these studies begin with a de novo transcriptome assembly, which is a technically complicated process involving several discrete steps. The Oyster River Protocol (ORP), described here, implements a standardized and benchmarked set of bioinformatic processes, resulting in an assembly with enhanced qualities over other standard assembly methods. Specifically, ORP produced assemblies have higher Detonate and TransRate scores and mapping rates, which is largely a product of the fact that it leverages a multi-assembler and kmer assembly process, thereby bypassing the shortcomings of any one approach. These improvements are important, as previously unassembled transcripts are included in ORP assemblies, resulting in a significant enhancement of the power of downstream analysis. Further, as part of this study, I show that assembly quality is unrelated with the number of reads generated, above 30 million reads. Code Availability: The version controlled open-source code is available at https://github.com/macmanes-lab/Oyster_River_Protocol. Instructions for software installation and use, and other details are available at http://oyster-river-protocol.rtfd.org/.
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Project description:The assembly of multiple genomes from mixed sequence reads is a bottleneck in metagenomic analysis. A single-genome assembly program (assembler) is not capable of resolving metagenome sequences, so assemblers designed specifically for metagenomics have been developed. MetaVelvet is an extension of the single-genome assembler Velvet. It has been proved to generate assemblies with higher N50 scores and higher quality than single-genome assemblers such as Velvet and SOAPdenovo when applied to metagenomic sequence reads and is frequently used in this research community. One important open problem for MetaVelvet is its low accuracy and sensitivity in detecting chimeric nodes in the assembly (de Bruijn) graph, which prevents the generation of longer contigs and scaffolds. We have tackled this problem of classifying chimeric nodes using supervised machine learning to significantly improve the performance of MetaVelvet and developed a new tool, called MetaVelvet-SL. A Support Vector Machine is used for learning the classification model based on 94 features extracted from candidate nodes. In extensive experiments, MetaVelvet-SL outperformed the original MetaVelvet and other state-of-the-art metagenomic assemblers, IDBA-UD, Ray Meta and Omega, to reconstruct accurate longer assemblies with higher N50 scores for both simulated data sets and real data sets of human gut microbial sequences.
| S-EPMC4379979 | biostudies-literature