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Asset Tag: bioinformatics

September 22, 2019

Improving PacBio long read accuracy by short read alignment.

The recent development of third generation sequencing (TGS) generates much longer reads than second generation sequencing (SGS) and thus provides a chance to solve problems that are difficult to study through SGS alone. However, higher raw read error rates are an intrinsic drawback in most TGS technologies. Here we present a computational method, LSC, to perform error correction of TGS long reads (LR) by SGS short reads (SR). Aiming to reduce the error rate in homopolymer runs in the main TGS platform, the PacBio® RS, LSC applies a homopolymer compression (HC) transformation strategy to increase the sensitivity of SR-LR alignment without scarifying alignment accuracy. We applied LSC to 100,000 PacBio long reads from human brain cerebellum RNA-seq data and 64 million single-end 75 bp reads from human brain RNA-seq data. The results show LSC can correct PacBio long reads to reduce the error rate by more than 3 folds. The improved accuracy greatly benefits many downstream analyses, such as directional gene isoform detection in RNA-seq study. Compared with another hybrid correction tool, LSC can achieve over double the sensitivity and similar specificity.

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September 22, 2019

BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly.

The problem of de-novo assembly for metagenomes using only long reads is gaining attention. We study whether post-processing metagenomic assemblies with the original input long reads can result in quality improvement. Previous approaches have focused on pre-processing reads and optimizing assemblers. BIGMAC takes an alternative perspective to focus on the post-processing step.Using both the assembled contigs and original long reads as input, BIGMAC first breaks the contigs at potentially mis-assembled locations and subsequently scaffolds contigs. Our experiments on metagenomes assembled from long reads show that BIGMAC can improve assembly quality by reducing the number of mis-assemblies while maintaining or increasing N50 and N75. Moreover, BIGMAC shows the largest N75 to number of mis-assemblies ratio on all tested datasets when compared to other post-processing tools. BIGMAC demonstrates the effectiveness of the post-processing approach in improving the quality of metagenomic assemblies.

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September 22, 2019

PRAPI: post-transcriptional regulation analysis pipeline for Iso-Seq.

The single-molecule real-time (SMRT) isoform sequencing (Iso-Seq) based on Pacific Bioscience (PacBio) platform has received increasing attention for its ability to explore full-length isoforms. Thus, comprehensive tools for Iso-Seq bioinformatics analysis are extremely useful. Here, we present a one-stop solution for Iso-Seq analysis, called PRAPI to analyze alternative transcription initiation (ATI), alternative splicing (AS), alternative cleavage and polyadenylation (APA), natural antisense transcripts (NAT), and circular RNAs (circRNAs) comprehensively. PRAPI is capable of combining Iso-Seq full-length isoforms with short read data, such as RNA-Seq or polyadenylation site sequencing (PAS-seq) for differential expression analysis of NAT, AS, APA and circRNAs. Furthermore, PRAPI can annotate new genes and correct mis-annotated genes when gene annotation is available. Finally, PRAPI generates high-quality vector graphics to visualize and highlight the Iso-Seq results.The Dockerfile of PRAPI is available at http://www.bioinfor.org/tool/PRAPI.lfgu@fafu.edu.cn.

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September 22, 2019

Metagenomic binning and association of plasmids with bacterial host genomes using DNA methylation.

Shotgun metagenomics methods enable characterization of microbial communities in human microbiome and environmental samples. Assembly of metagenome sequences does not output whole genomes, so computational binning methods have been developed to cluster sequences into genome ‘bins’. These methods exploit sequence composition, species abundance, or chromosome organization but cannot fully distinguish closely related species and strains. We present a binning method that incorporates bacterial DNA methylation signatures, which are detected using single-molecule real-time sequencing. Our method takes advantage of these endogenous epigenetic barcodes to resolve individual reads and assembled contigs into species- and strain-level bins. We validate our method using synthetic and real microbiome sequences. In addition to genome binning, we show that our method links plasmids and other mobile genetic elements to their host species in a real microbiome sample. Incorporation of DNA methylation information into shotgun metagenomics analyses will complement existing methods to enable more accurate sequence binning.

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September 22, 2019

Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data.

The use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment. However, these samples contain genomic sequences from various sources that complicate the identification of pathogens.Here we present Clinical PathoScope, a pipeline to rapidly and accurately remove host contamination, isolate microbial reads, and identify potential disease-causing pathogens. We have accomplished three essential tasks in the development of Clinical PathoScope. First, we developed an optimized framework for pathogen identification using a computational subtraction methodology in concordance with read trimming and ambiguous read reassignment. Second, we have demonstrated the ability of our approach to identify multiple pathogens in a single clinical sample, accurately identify pathogens at the subspecies level, and determine the nearest phylogenetic neighbor of novel or highly mutated pathogens using real clinical sequencing data. Finally, we have shown that Clinical PathoScope outperforms previously published pathogen identification methods with regard to computational speed, sensitivity, and specificity.Clinical PathoScope is the only pathogen identification method currently available that can identify multiple pathogens from mixed samples and distinguish between very closely related species and strains in samples with very few reads per pathogen. Furthermore, Clinical PathoScope does not rely on genome assembly and thus can more rapidly complete the analysis of a clinical sample when compared with current assembly-based methods. Clinical PathoScope is freely available at: http://sourceforge.net/projects/pathoscope/.

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September 22, 2019

MetaSort untangles metagenome assembly by reducing microbial community complexity.

Most current approaches to analyse metagenomic data rely on reference genomes. Novel microbial communities extend far beyond the coverage of reference databases and de novo metagenome assembly from complex microbial communities remains a great challenge. Here we present a novel experimental and bioinformatic framework, metaSort, for effective construction of bacterial genomes from metagenomic samples. MetaSort provides a sorted mini-metagenome approach based on flow cytometry and single-cell sequencing methodologies, and employs new computational algorithms to efficiently recover high-quality genomes from the sorted mini-metagenome by the complementary of the original metagenome. Through extensive evaluations, we demonstrated that metaSort has an excellent and unbiased performance on genome recovery and assembly. Furthermore, we applied metaSort to an unexplored microflora colonized on the surface of marine kelp and successfully recovered 75 high-quality genomes at one time. This approach will greatly improve access to microbial genomes from complex or novel communities.

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September 22, 2019

Interactive analysis of Long-read RNA isoforms with Iso-Seq Browser

Background: Long-read RNA sequencing, such as Pacific Biosciences Iso-Seq method, enables generation of sequencing reads that are 10 kilobases or even longer. These reads are ideal for discovering splice junctions and resolving full-length gene transcripts without time-consuming and error-prone techniques such as transcript assembly and junction inference. Results: Iso-Seq Browser is a Web-based visual analytics tool for long-read RNA sequencing data produced by Pacific Biosciences isoform sequencing (Iso-Seq) techniques. Key features of the Iso-Seq Browser are: 1) an exon-only web-based interface with zooming and exon highlighting for exploring reference gene transcripts and novel gene isoforms, 2) automated grouping of transcripts and isoforms by similarity, 3) many customization features for data exploration and creating publication ready figures, and 4) exporting selected isoforms into fasta files for further analysis. Iso-Seq Browser is written in Python using several scientific libraries. The application and analyses described in this paper are freely available to both academic and commercial users at https://github.com/goeckslab/isoseq-browser Conclusions: Iso-Seq Browser enables interactive genome-wide visual analysis of long RNA sequence reads. Through visualization, highlighting, clustering, and filtering of gene isoforms, ISB makes it simple to identify novel isoforms and novel isoform features such as exons, introns and untranslated regions.

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September 22, 2019

CATCh, an ensemble classifier for chimera detection in 16S rRNA sequencing studies.

In ecological studies, microbial diversity is nowadays mostly assessed via the detection of phylogenetic marker genes, such as 16S rRNA. However, PCR amplification of these marker genes produces a significant amount of artificial sequences, often referred to as chimeras. Different algorithms have been developed to remove these chimeras, but efforts to combine different methodologies are limited. Therefore, two machine learning classifiers (reference-based and de novo CATCh) were developed by integrating the output of existing chimera detection tools into a new, more powerful method. When comparing our classifiers with existing tools in either the reference-based or de novo mode, a higher performance of our ensemble method was observed on a wide range of sequencing data, including simulated, 454 pyrosequencing, and Illumina MiSeq data sets. Since our algorithm combines the advantages of different individual chimera detection tools, our approach produces more robust results when challenged with chimeric sequences having a low parent divergence, short length of the chimeric range, and various numbers of parents. Additionally, it could be shown that integrating CATCh in the preprocessing pipeline has a beneficial effect on the quality of the clustering in operational taxonomic units. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

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September 22, 2019

Alternative polyadenylation: methods, findings, and impacts.

Alternative polyadenylation (APA), a phenomenon that RNA molecules with different 3′ ends originate from distinct polyadenylation sites of a single gene, is emerging as a mechanism widely used to regulate gene expression. In the present review, we first summarized various methods prevalently adopted in APA study, mainly focused on the next-generation sequencing (NGS)-based techniques specially designed for APA identification, the related bioinformatics methods, and the strategies for APA study in single cells. Then we summarized the main findings and advances so far based on these methods, including the preferences of alternative polyA (pA) site, the biological processes involved, and the corresponding consequences. We especially categorized the APA changes discovered so far and discussed their potential functions under given conditions, along with the possible underlying molecular mechanisms. With more in-depth studies on extensive samples, more signatures and functions of APA will be revealed, and its diverse roles will gradually heave in sight. Copyright © 2017 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

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September 22, 2019

PhyloPythiaS+: a self-training method for the rapid reconstruction of low-ranking taxonomic bins from metagenomes.

Background. Metagenomics is an approach for characterizing environmental microbial communities in situ, it allows their functional and taxonomic characterization and to recover sequences from uncultured taxa. This is often achieved by a combination of sequence assembly and binning, where sequences are grouped into ‘bins’ representing taxa of the underlying microbial community. Assignment to low-ranking taxonomic bins is an important challenge for binning methods as is scalability to Gb-sized datasets generated with deep sequencing techniques. One of the best available methods for species bins recovery from deep-branching phyla is the expert-trained PhyloPythiaS package, where a human expert decides on the taxa to incorporate in the model and identifies ‘training’ sequences based on marker genes directly from the sample. Due to the manual effort involved, this approach does not scale to multiple metagenome samples and requires substantial expertise, which researchers who are new to the area do not have. Results. We have developed PhyloPythiaS+, a successor to our PhyloPythia(S) software. The new (+) component performs the work previously done by the human expert. PhyloPythiaS+ also includes a new k-mer counting algorithm, which accelerated the simultaneous counting of 4-6-mers used for taxonomic binning 100-fold and reduced the overall execution time of the software by a factor of three. Our software allows to analyze Gb-sized metagenomes with inexpensive hardware, and to recover species or genera-level bins with low error rates in a fully automated fashion. PhyloPythiaS+ was compared to MEGAN, taxator-tk, Kraken and the generic PhyloPythiaS model. The results showed that PhyloPythiaS+ performs especially well for samples originating from novel environments in comparison to the other methods. Availability. PhyloPythiaS+ in a virtual machine is available for installation under Windows, Unix systems or OS X on: https://github.com/algbioi/ppsp/wiki.

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September 22, 2019

L_RNA_scaffolder: scaffolding genomes with transcripts.

Generation of large mate-pair libraries is necessary for de novo genome assembly but the procedure is complex and time-consuming. Furthermore, in some complex genomes, it is hard to increase the N50 length even with large mate-pair libraries, which leads to low transcript coverage. Thus, it is necessary to develop other simple scaffolding approaches, to at least solve the elongation of transcribed fragments.We describe L_RNA_scaffolder, a novel genome scaffolding method that uses long transcriptome reads to order, orient and combine genomic fragments into larger sequences. To demonstrate the accuracy of the method, the zebrafish genome was scaffolded. With expanded human transcriptome data, the N50 of human genome was doubled and L_RNA_scaffolder out-performed most scaffolding results by existing scaffolders which employ mate-pair libraries. In these two examples, the transcript coverage was almost complete, especially for long transcripts. We applied L_RNA_scaffolder to the highly polymorphic pearl oyster draft genome and the gene model length significantly increased.The simplicity and high-throughput of RNA-seq data makes this approach suitable for genome scaffolding. L_RNA_scaffolder is available at http://www.fishbrowser.org/software/L_RNA_scaffolder.

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September 22, 2019

Bayesian nonparametric discovery of isoforms and individual specific quantification.

Most human protein-coding genes can be transcribed into multiple distinct mRNA isoforms. These alternative splicing patterns encourage molecular diversity, and dysregulation of isoform expression plays an important role in disease etiology. However, isoforms are difficult to characterize from short-read RNA-seq data because they share identical subsequences and occur in different frequencies across tissues and samples. Here, we develop BIISQ, a Bayesian nonparametric model for isoform discovery and individual specific quantification from short-read RNA-seq data. BIISQ does not require isoform reference sequences but instead estimates an isoform catalog shared across samples. We use stochastic variational inference for efficient posterior estimates and demonstrate superior precision and recall for simulations compared to state-of-the-art isoform reconstruction methods. BIISQ shows the most gains for low abundance isoforms, with 36% more isoforms correctly inferred at low coverage versus a multi-sample method and 170% more versus single-sample methods. We estimate isoforms in the GEUVADIS RNA-seq data and validate inferred isoforms by associating genetic variants with isoform ratios.

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September 22, 2019

Computational identification of novel genes: current and future perspectives.

While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been shown to follow a duplication and divergence process. Their discovery called for an extension of the historical hypotheses about gene origination. Besides the theoretical breakthrough, increasing evidence accumulated that novel genes play important roles in evolutionary processes, including adaptation and speciation events. Different techniques are available to identify genes and classify them as novel. Their classification as novel is usually based on their similarity to known genes, or lack thereof, detected by comparative genomics or against databases. Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. Identification of novel genes remains however a challenging task. With the constant software and technologies updates, no gold standard, and no available benchmark, evaluation and characterization of genomic novelty is a vibrant field. In this review, the classical and state-of-the-art tools for gene prediction are introduced. The current methods for novel gene detection are presented; the methodological strategies and their limits are discussed along with perspective approaches for further studies.

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September 22, 2019

SparseIso: a novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data.

Recent advances in high-throughput RNA sequencing (RNA-seq) technologies have made it possible to reconstruct the full transcriptome of various types of cells. It is important to accurately assemble transcripts or identify isoforms for an improved understanding of molecular mechanisms in biological systems.We have developed a novel Bayesian method, SparseIso, to reliably identify spliced isoforms from RNA-seq data. A spike-and-slab prior is incorporated into the Bayesian model to enforce the sparsity for isoform identification, effectively alleviating the problem of overfitting. A Gibbs sampling procedure is further developed to simultaneously identify and quantify transcripts from RNA-seq data. With the sampling approach, SparseIso estimates the joint distribution of all candidate transcripts, resulting in a significantly improved performance in detecting lowly expressed transcripts and multiple expressed isoforms of genes. Both simulation study and real data analysis have demonstrated that the proposed SparseIso method significantly outperforms existing methods for improved transcript assembly and isoform identification.The SparseIso package is available at http://github.com/henryxushi/SparseIso.xuan@vt.edu.Supplementary data are available at Bioinformatics online.© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

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