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Asset Tag: bioinformatics

April 21, 2020

Platanus-allee is a de novo haplotype assembler enabling a comprehensive access to divergent heterozygous regions.

The ultimate goal for diploid genome determination is to completely decode homologous chromosomes independently, and several phasing programs from consensus sequences have been developed. These methods work well for lowly heterozygous genomes, but the manifold species have high heterozygosity. Additionally, there are highly divergent regions (HDRs), where the haplotype sequences differ considerably. Because HDRs are likely to direct various interesting biological phenomena, many genomic analysis targets fall within these regions. However, they cannot be accessed by existing phasing methods, and we have to adopt costly traditional methods. Here, we develop a de novo haplotype assembler, Platanus-allee ( http://platanus.bio.titech.ac.jp/platanus2 ), which initially constructs each haplotype sequence and then untangles the assembly graphs utilizing sequence links and synteny information. A comprehensive benchmark analysis reveals that Platanus-allee exhibits high recall and precision, particularly for HDRs. Using this approach, previously unknown HDRs are detected in the human genome, which may uncover novel aspects of genome variability.

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April 21, 2020

Deep convolutional neural networks for accurate somatic mutation detection.

Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities. NeuSomatic summarizes sequence alignments into small matrices and incorporates more than a hundred features to capture mutation signals effectively. It can be used universally as a stand-alone somatic mutation detection method or with an ensemble of existing methods to achieve the highest accuracy.

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April 21, 2020

A multi-task convolutional deep neural network for variant calling in single molecule sequencing.

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5-15%. Meeting this demand, we developed Clairvoyante, a multi-task five-layer convolutional neural network model for predicting variant type (SNP or indel), zygosity, alternative allele and indel length from aligned reads. For the well-characterized NA12878 human sample, Clairvoyante achieves 99.67, 95.78, 90.53% F1-score on 1KP common variants, and 98.65, 92.57, 87.26% F1-score for whole-genome analysis, using Illumina, PacBio, and Oxford Nanopore data, respectively. Training on a second human sample shows Clairvoyante is sample agnostic and finds variants in less than 2?h on a standard server. Furthermore, we present 3,135 variants that are missed using Illumina but supported independently by both PacBio and Oxford Nanopore reads. Clairvoyante is available open-source ( https://github.com/aquaskyline/Clairvoyante ), with modules to train, utilize and visualize the model.

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April 21, 2020

Sequence properties of certain GC rich avian genes, their origins and absence from genome assemblies: case studies.

More and more eukaryotic genomes are sequenced and assembled, most of them presented as a complete model in which missing chromosomal regions are filled by Ns and where a few chromosomes may be lacking. Avian genomes often contain sequences with high GC content, which has been hypothesized to be at the origin of many missing sequences in these genomes. We investigated features of these missing sequences to discover why some may not have been integrated into genomic libraries and/or sequenced.The sequences of five red jungle fowl cDNA models with high GC content were used as queries to search publicly available datasets of Illumina and Pacbio sequencing reads. These were used to reconstruct the leptin, TNFa, MRPL52, PCP2 and PET100 genes, all of which are absent from the red jungle fowl genome model. These gene sequences displayed elevated GC contents, had intron sizes that were sometimes larger than non-avian orthologues, and had non-coding regions that contained numerous tandem and inverted repeat sequences with motifs able to assemble into stable G-quadruplexes and intrastrand dyadic structures. Our results suggest that Illumina technology was unable to sequence the non-coding regions of these genes. On the other hand, PacBio technology was able to sequence these regions, but with dramatically lower efficiency than would typically be expected.High GC content was not the principal reason why numerous GC-rich regions of avian genomes are missing from genome assembly models. Instead, it is the presence of tandem repeats containing motifs capable of assembling into very stable secondary structures that is likely responsible.

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April 21, 2020

The importance of genome sequence quality to microbial comparative genomics.

The quality of microbial genome sequences has been a concern ever since the emergence of genome sequencing. The quality of the genome assemblies is dependent on the sequencing technology used and the aims for which the sequence was generated. Novel sequencing and bioinformatics technologies are not intrinsically better than the older technologies, although they are generally more efficient. In this correspondence, the importance for comparative genomics of additional manual assembly efforts over autoassembly and careful annotation is emphasized.

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April 21, 2020

DiscoverY: a classifier for identifying Y chromosome sequences in male assemblies.

Although the Y chromosome plays an important role in male sex determination and fertility, it is currently understudied due to its haploid and repetitive nature. Methods to isolate Y-specific contigs from a whole-genome assembly broadly fall into two categories. The first involves retrieving Y-contigs using proportion sharing with a female, but such a strategy is prone to false positives in the absence of a high-quality, complete female reference. A second strategy uses the ratio of depth of coverage from male and female reads to select Y-contigs, but such a method requires high-depth sequencing of a female and cannot utilize existing female references.We develop a k-mer based method called DiscoverY, which combines proportion sharing with female with depth of coverage from male reads to classify contigs as Y-chromosomal. We evaluate the performance of DiscoverY on human and gorilla genomes, across different sequencing platforms including Illumina, 10X, and PacBio. In the cases where the male and female data are of high quality, DiscoverY has a high precision and recall and outperforms existing methods. For cases when a high quality female reference is not available, we quantify the effect of using draft reference or even just raw sequencing reads from a female.DiscoverY is an effective method to isolate Y-specific contigs from a whole-genome assembly. However, regions homologous to the X chromosome remain difficult to detect.

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April 21, 2020

GAPPadder: a sensitive approach for closing gaps on draft genomes with short sequence reads.

Closing gaps in draft genomes is an important post processing step in genome assembly. It leads to more complete genomes, which benefits downstream genome analysis such as annotation and genotyping. Several tools have been developed for gap closing. However, these tools don’t fully utilize the information contained in the sequence data. For example, while it is known that many gaps are caused by genomic repeats, existing tools often ignore many sequence reads that originate from a repeat-related gap.We compare GAPPadder with GapCloser, GapFiller and Sealer on one bacterial genome, human chromosome 14 and the human whole genome with paired-end and mate-paired reads with both short and long insert sizes. Empirical results show that GAPPadder can close more gaps than these existing tools. Besides closing gaps on draft genomes assembled only from short sequence reads, GAPPadder can also be used to close gaps for draft genomes assembled with long reads. We show GAPPadder can close gaps on the bed bug genome and the Asian sea bass genome that are assembled partially and fully with long reads respectively. We also show GAPPadder is efficient in both time and memory usage.In this paper, we propose a new approach called GAPPadder for gap closing. The main advantage of GAPPadder is that it uses more information in sequence data for gap closing. In particular, GAPPadder finds and uses reads that originate from repeat-related gaps. We show that these repeat-associated reads are useful for gap closing, even though they are ignored by all existing tools. Other main features of GAPPadder include utilizing the information in sequence reads with different insert sizes and performing two-stage local assembly of gap sequences. The results show that our method can close more gaps than several existing tools. The software tool, GAPPadder, is available for download at https://github.com/Reedwarbler/GAPPadder .

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April 21, 2020

Magic-BLAST, an accurate RNA-seq aligner for long and short reads.

Next-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, especially with long reads. We introduce Magic-BLAST, a new aligner based on ideas from the Magic pipeline.Magic-BLAST uses innovative techniques that include the optimization of a spliced alignment score and selective masking during seed selection. We evaluate the performance of Magic-BLAST to accurately map short or long sequences and its ability to discover introns on real RNA-seq data sets from PacBio, Roche and Illumina runs, and on six benchmarks, and compare it to other popular aligners. Additionally, we look at alignments of human idealized RefSeq mRNA sequences perfectly matching the genome.We show that Magic-BLAST is the best at intron discovery over a wide range of conditions and the best at mapping reads longer than 250 bases, from any platform. It is versatile and robust to high levels of mismatches or extreme base composition, and reasonably fast. It can align reads to a BLAST database or a FASTA file. It can accept a FASTQ file as input or automatically retrieve an accession from the SRA repository at the NCBI.

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April 21, 2020

Exploring the landscape of focal amplifications in cancer using AmpliconArchitect.

Focal oncogene amplification and rearrangements drive tumor growth and evolution in multiple cancer types. We present AmpliconArchitect (AA), a tool to reconstruct the fine structure of focally amplified regions using whole genome sequencing (WGS) and validate it extensively on multiple simulated and real datasets, across a wide range of coverage and copy numbers. Analysis of AA-reconstructed amplicons in a pan-cancer dataset reveals many novel properties of copy number amplifications in cancer. These findings support a model in which focal amplifications arise due to the formation and replication of extrachromosomal DNA. Applying AA to 68 viral-mediated cancer samples, we identify a large fraction of amplicons with specific structural signatures suggestive of hybrid, human-viral extrachromosomal DNA. AA reconstruction, integrated with metaphase fluorescence in situ hybridization (FISH) and PacBio sequencing on the cell-line UPCI:SCC090 confirm the extrachromosomal origin and fine structure of a Forkhead box E1 (FOXE1)-containing hybrid amplicon.

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April 21, 2020

Illumina error correction near highly repetitive DNA regions improves de novo genome assembly.

Several standalone error correction tools have been proposed to correct sequencing errors in Illumina data in order to facilitate de novo genome assembly. However, in a recent survey, we showed that state-of-the-art assemblers often did not benefit from this pre-correction step. We found that many error correction tools introduce new errors in reads that overlap highly repetitive DNA regions such as low-complexity patterns or short homopolymers, ultimately leading to a more fragmented assembly.We propose BrownieCorrector, an error correction tool for Illumina sequencing data that focuses on the correction of only those reads that overlap short DNA patterns that are highly repetitive in the genome. BrownieCorrector extracts all reads that contain such a pattern and clusters them into different groups using a community detection algorithm that takes into account both the sequence similarity between overlapping reads and their respective paired-end reads. Each cluster holds reads that originate from the same genomic region and hence each cluster can be corrected individually, thus providing a consistent correction for all reads within that cluster.BrownieCorrector is benchmarked using six real Illumina datasets for different eukaryotic genomes. The prior use of BrownieCorrector improves assembly results over the use of uncorrected reads in all cases. In comparison with other error correction tools, BrownieCorrector leads to the best assembly results in most cases even though less than 2% of the reads within a dataset are corrected. Additionally, we investigate the impact of error correction on hybrid assembly where the corrected Illumina reads are supplemented with PacBio data. Our results confirm that BrownieCorrector improves the quality of hybrid genome assembly as well. BrownieCorrector is written in standard C++11 and released under GPL license. BrownieCorrector relies on multithreading to take advantage of multi-core/multi-CPU systems. The source code is available at https://github.com/biointec/browniecorrector .

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April 21, 2020

Improving the sensitivity of long read overlap detection using grouped short k-mer matches.

Single-molecule, real-time sequencing (SMRT) developed by Pacific BioSciences produces longer reads than second-generation sequencing technologies such as Illumina. The increased read length enables PacBio sequencing to close gaps in genome assembly, reveal structural variations, and characterize the intra-species variations. It also holds the promise to decipher the community structure in complex microbial communities because long reads help metagenomic assembly. One key step in genome assembly using long reads is to quickly identify reads forming overlaps. Because PacBio data has higher sequencing error rate and lower coverage than popular short read sequencing technologies (such as Illumina), efficient detection of true overlaps requires specially designed algorithms. In particular, there is still a need to improve the sensitivity of detecting small overlaps or overlaps with high error rates in both reads. Addressing this need will enable better assembly for metagenomic data produced by third-generation sequencing technologies.In this work, we designed and implemented an overlap detection program named GroupK, for third-generation sequencing reads based on grouped k-mer hits. While using k-mer hits for detecting reads’ overlaps has been adopted by several existing programs, our method uses a group of short k-mer hits satisfying statistically derived distance constraints to increase the sensitivity of small overlap detection. Grouped k-mer hit was originally designed for homology search. We are the first to apply group hit for long read overlap detection. The experimental results of applying our pipeline to both simulated and real third-generation sequencing data showed that GroupK enables more sensitive overlap detection, especially for datasets of low sequencing coverage.GroupK is best used for detecting small overlaps for third-generation sequencing data. It provides a useful supplementary tool to existing ones for more sensitive and accurate overlap detection. The source code is freely available at https://github.com/Strideradu/GroupK .

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April 21, 2020

FGMP: assessing fungal genome completeness

Background: Inexpensive high-throughput DNA sequencing has democratized access to genetic information for most organisms so that research utilizing a genome or transcriptome of an organism is not limited to model systems. However, the quality of the assemblies of sampled genomes can vary greatly which hampers utility for comparisons and meaningful interpretation. The uncertainty of the completeness of a given genome sequence can limit feasibility of asserting patterns of high rates of gene loss reported in many lineages. Results: We propose a computational framework and sequence resource for assessing completeness of fungal genomes called FGMP (Fungal Genome Mapping Project). Our approach is based on evolutionary conserved sets of proteins and DNA elements and is applicable to various types of genomic data. We present a comparison of FGMP and state-of-the-art methods for genome completeness assessment utilizing 246 genome assemblies of fungi. We discuss genome assembly improvements/degradations in 57 cases where assemblies have been updated, as recorded by NCBI assembly archive. Conclusion: FGMP is an accurate tool for quantifying level of completion from fungal genomic data. It is particularly useful for non-model organisms without reference genomes and can be used directly on unassembled reads, which can help reducing genome sequencing costs.

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April 21, 2020

Haplotype-aware diplotyping from noisy long reads.

Current genotyping approaches for single-nucleotide variations rely on short, accurate reads from second-generation sequencing devices. Presently, third-generation sequencing platforms are rapidly becoming more widespread, yet approaches for leveraging their long but error-prone reads for genotyping are lacking. Here, we introduce a novel statistical framework for the joint inference of haplotypes and genotypes from noisy long reads, which we term diplotyping. Our technique takes full advantage of linkage information provided by long reads. We validate hundreds of thousands of candidate variants that have not yet been included in the high-confidence reference set of the Genome-in-a-Bottle effort.

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April 21, 2020

Tandem-genotypes: robust detection of tandem repeat expansions from long DNA reads.

Tandemly repeated DNA is highly mutable and causes at least 31 diseases, but it is hard to detect pathogenic repeat expansions genome-wide. Here, we report robust detection of human repeat expansions from careful alignments of long but error-prone (PacBio and nanopore) reads to a reference genome. Our method is robust to systematic sequencing errors, inexact repeats with fuzzy boundaries, and low sequencing coverage. By comparing to healthy controls, we prioritize pathogenic expansions within the top 10 out of 700,000 tandem repeats in whole genome sequencing data. This may help to elucidate the many genetic diseases whose causes remain unknown.

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April 21, 2020

IMOS: improved Meta-aligner and Minimap2 On Spark.

Long reads provide valuable information regarding the sequence composition of genomes. Long reads are usually very noisy which renders their alignments on the reference genome a daunting task. It may take days to process datasets enough to sequence a human genome on a single node. Hence, it is of primary importance to have an aligner which can operate on distributed clusters of computers with high performance in accuracy and speed.In this paper, we presented IMOS, an aligner for mapping noisy long reads to the reference genome. It can be used on a single node as well as on distributed nodes. In its single-node mode, IMOS is an Improved version of Meta-aligner (IM) enhancing both its accuracy and speed. IM is up to 6x faster than the original Meta-aligner. It is also implemented to run IM and Minimap2 on Apache Spark for deploying on a cluster of nodes. Moreover, multi-node IMOS is faster than SparkBWA while executing both IM (1.5x) and Minimap2 (25x).In this paper, we purposed an architecture for mapping long reads to a reference. Due to its implementation, IMOS speed can increase almost linearly with respect to the number of nodes in a cluster. Also, it is a multi-platform application able to operate on Linux, Windows, and macOS.

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