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Asset Tag: bioinformatics

September 22, 2019

NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data.

Structural variants (SVs) in human genomes are implicated in a variety of human diseases. Long-read sequencing delivers much longer read lengths than short-read sequencing and may greatly improve SV detection. However, due to the relatively high cost of long-read sequencing, it is unclear what coverage is needed and how to optimally use the aligners and SV callers.In this study, we developed NextSV, a meta-caller to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purposes. We evaluated SV calling performance of NextSV under different PacBio coverages on two personal genomes, NA12878 and HX1. Our results showed that, compared with running any single SV caller, NextSV stringent call set had higher precision and balanced accuracy (F1 score) while NextSV sensitive call set had a higher recall. At 10X coverage, the recall of NextSV sensitive call set was 93.5 to 94.1% for deletions and 87.9 to 93.2% for insertions, indicating that ~10X coverage might be an optimal coverage to use in practice, considering the balance between the sequencing costs and the recall rates. We further evaluated the Mendelian errors on an Ashkenazi Jewish trio dataset.Our results provide useful guidelines for SV detection from low coverage whole-genome PacBio data and we expect that NextSV will facilitate the analysis of SVs on long-read sequencing data.

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September 22, 2019

NPBSS: a new PacBio sequencing simulator for generating the continuous long reads with an empirical model.

PacBio sequencing platform offers longer read lengths than the second-generation sequencing technologies. It has revolutionized de novo genome assembly and enabled the automated reconstruction of reference-quality genomes. Due to its extremely wide range of application areas, fast sequencing simulation systems with high fidelity are in great demand to facilitate the development and comparison of subsequent analysis tools. Although there are several available simulators (e.g., PBSIM, SimLoRD and FASTQSim) that target the specific generation of PacBio libraries, the error rate of simulated sequences is not well matched to the quality value of raw PacBio datasets, especially for PacBio’s continuous long reads (CLR).By analyzing the characteristic features of CLR data from PacBio SMRT (single molecule real time) sequencing, we developed a new PacBio sequencing simulator (called NPBSS) for producing CLR reads. NPBSS simulator firstly samples the read sequences according to the read length logarithmic normal distribution, and choses different base quality values with different proportions. Then, NPBSS computes the overall error probability of each base in the read sequence with an empirical model, and calculates the deletion, substitution and insertion probabilities with the overall error probability to generate the PacBio CLR reads. Alignment results demonstrate that NPBSS fits the error rate of the PacBio CLR reads better than PBSIM and FASTQSim. In addition, the assembly results also show that simulated sequences of NPBSS are more like real PacBio CLR data.NPBSS simulator is convenient to use with efficient computation and flexible parameters setting. Its generating PacBio CLR reads are more like real PacBio datasets.

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September 22, 2019

A transposable element annotation pipeline and expression analysis reveal potentially active elements in the microalga Tisochrysis lutea.

Transposable elements (TEs) are mobile DNA sequences known as drivers of genome evolution. Their impacts have been widely studied in animals, plants and insects, but little is known about them in microalgae. In a previous study, we compared the genetic polymorphisms between strains of the haptophyte microalga Tisochrysis lutea and suggested the involvement of active autonomous TEs in their genome evolution.To identify potentially autonomous TEs, we designed a pipeline named PiRATE (Pipeline to Retrieve and Annotate Transposable Elements, download: https://doi.org/10.17882/51795 ), and conducted an accurate TE annotation on a new genome assembly of T. lutea. PiRATE is composed of detection, classification and annotation steps. Its detection step combines multiple, existing analysis packages representing all major approaches for TE detection and its classification step was optimized for microalgal genomes. The efficiency of the detection and classification steps was evaluated with data on the model species Arabidopsis thaliana. PiRATE detected 81% of the TE families of A. thaliana and correctly classified 75% of them. We applied PiRATE to T. lutea genomic data and established that its genome contains 15.89% Class I and 4.95% Class II TEs. In these, 3.79 and 17.05% correspond to potentially autonomous and non-autonomous TEs, respectively. Annotation data was combined with transcriptomic and proteomic data to identify potentially active autonomous TEs. We identified 17 expressed TE families and, among these, a TIR/Mariner and a TIR/hAT family were able to synthesize their transposase. Both these TE families were among the three highest expressed genes in a previous transcriptomic study and are composed of highly similar copies throughout the genome of T. lutea. This sum of evidence reveals that both these TE families could be capable of transposing or triggering the transposition of potential related MITE elements.This manuscript provides an example of a de novo transposable element annotation of a non-model organism characterized by a fragmented genome assembly and belonging to a poorly studied phylum at genomic level. Integration of multi-omics data enabled the discovery of potential mobile TEs and opens the way for new discoveries on the role of these repeated elements in genomic evolution of microalgae.

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September 22, 2019

Progressive approach for SNP calling and haplotype assembly using single molecular sequencing data.

Haplotype information is essential to the complete description and interpretation of genomes, genetic diversity and genetic ancestry. The new technologies can provide Single Molecular Sequencing (SMS) data that cover about 90% of positions over chromosomes. However, the SMS data has a higher error rate comparing to 1% error rate for short reads. Thus, it becomes very difficult for SNP calling and haplotype assembly using SMS reads. Most existing technologies do not work properly for the SMS data.In this paper, we develop a progressive approach for SNP calling and haplotype assembly that works very well for the SMS data. Our method can handle more than 200 million non-N bases on Chromosome 1 with millions of reads, more than 100 blocks, each of which contains more than 2 million bases and more than 3K SNP sites on average. Experiment results show that the false discovery rate and false negative rate for our method are 15.7 and 11.0% on NA12878, and 16.5 and 11.0% on NA24385. Moreover, the overall switch errors for our method are 7.26 and 5.21 with average 3378 and 5736 SNP sites per block on NA12878 and NA24385, respectively. Here, we demonstrate that SMS reads alone can generate a high quality solution for both SNP calling and haplotype assembly.Source codes and results are available at https://github.com/guofeieileen/SMRT/wiki/Software.

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September 22, 2019

Long-read sequencing data analysis for yeasts.

Long-read sequencing technologies have become increasingly popular due to their strengths in resolving complex genomic regions. As a leading model organism with small genome size and great biotechnological importance, the budding yeast Saccharomyces cerevisiae has many isolates currently being sequenced with long reads. However, analyzing long-read sequencing data to produce high-quality genome assembly and annotation remains challenging. Here, we present a modular computational framework named long-read sequencing data analysis for yeasts (LRSDAY), the first one-stop solution that streamlines this process. Starting from the raw sequencing reads, LRSDAY can produce chromosome-level genome assembly and comprehensive genome annotation in a highly automated manner with minimal manual intervention, which is not possible using any alternative tool available to date. The annotated genomic features include centromeres, protein-coding genes, tRNAs, transposable elements (TEs), and telomere-associated elements. Although tailored for S. cerevisiae, we designed LRSDAY to be highly modular and customizable, making it adaptable to virtually any eukaryotic organism. When applying LRSDAY to an S. cerevisiae strain, it takes ~41 h to generate a complete and well-annotated genome from ~100× Pacific Biosciences (PacBio) running the basic workflow with four threads. Basic experience working within the Linux command-line environment is recommended for carrying out the analysis using LRSDAY.

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September 22, 2019

A short note on dynamic programming in a band.

Third generation sequencing technologies generate long reads that exhibit high error rates, in particular for insertions and deletions which are usually the most difficult errors to cope with. The only exact algorithm capable of aligning sequences with insertions and deletions is a dynamic programming algorithm.In this note, for the sake of efficiency, we consider dynamic programming in a band. We show how to choose the band width in function of the long reads’ error rates, thus obtaining an [Formula: see text] algorithm in space and time. We also propose a procedure to decide whether this algorithm, when applied to semi-global alignments, provides the optimal score.We suggest that dynamic programming in a band is well suited to the problem of aligning long reads between themselves and can be used as a core component of methods for obtaining a consensus sequence from the long reads alone. The function implementing the dynamic programming algorithm in a band is available, as a standalone program, at: https://forgemia.inra.fr/jean-francois.gibrat/BAND_DYN_PROG.git.

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September 22, 2019

Genome Assembly.

Genome assembly uses sequence similarity to go from sequencing reads to longer contiguous sequences (contigs). Scaffolds are contigs linked together by gaps where the order and orientation of the contigs is known but the exact sequence connecting two contigs is unknown, represented by Ns which estimate the gap length. Here we describe recommendations for genome assembly for different sequencing technologies, describe organelle assembly, and review how to perform assembly quality control.

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September 22, 2019

Computational Modeling of Multidrug-Resistant Bacteria

Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology, and computational approaches have been increasingly employed to tackle this task. In this chapter, we describe current efforts by FIOCRUZ and partners to develop integrated computational models of multidrug-resistant bacteria. The bacterium chosen as the main focus of this effort is Pseudomonas aeruginosa, an opportunistic pathogen associated with a broad spectrum of infections in humans. Nowadays, P. aeruginosa is one of the main problems of healthcare-associated infections (HAI) in the world, because of its great capacity of survival in hospital environments and its intrinsic resistance to many antibiotics. Our overall research objective is to use integrated computational models to accurately predict a wide range of observable cellular behaviors of multidrug-resistant P. aeruginosa CCBH4851, which is a strain belonging to the clone ST277, endemic in Brazil. In this chapter, after a brief introduction to P. aeruginosa biology, we discuss the construction of metabolic and gene regulatory networks of P. aeruginosa CCBH 4851 from its genome. We also illustrate how these networks can be integrated into a single model, and we discuss methods for identifying potential therapeutic targets through integrated models.

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September 22, 2019

Diversity of hepatitis E virus genotype 3

Summary Hepatitis E virus genotype 3 (HEV-3) can lead to chronic infection in immunocompromised patients, and ribavirin is the treatment of choice. Recently, mutations in the polymerase gene have been associated with ribavirin failure but their frequency before treatment according to HEV-3 subtypes has not been studied on a large data set. We used single-molecule real-time sequencing technology to sequence 115 new complete genomes of HEV-3 infecting French patients. We analyzed phylogenetic relationships, the length of the polyproline region, and mutations in the HEV polymerase gene. Eighty-five (74%) were in the clade HEV-3efg, 28 (24%) in HEV-3chi clade, and 2 (2%) in HEV-3ra clade. Using automated partitioning of maximum likelihood phylogenetic trees, complete genomes were classified into subtypes. Polyproline region length differs within HEV-3 clades (from 189 to 315 nt). Investigating mutations in the polymerase gene, distinct polymorphisms between HEV-3 subtypes were found (G1634R in 95% of HEV-3e, G1634K in 56% of HEV-3ra, and V1479I in all HEV-3efg, clade HEV-3ra, and HEV-3k strains). Subtype-specific polymorphisms in the HEV-3 polymerase have been identified. Our study provides new complete genome sequences of HEV-3 that could be useful for comparing strains circulating in humans and the animal reservoir.

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September 22, 2019

genomeview – an extensible python-based genomics visualization engine

Visual inspection and analysis are integral to quality control, hypothesis generation, methods development and validation of genomic data. The richness and complexity of genomic data necessitates customized visualizations highlighting specific features of interest while hiding the often vast tide of irrelevant attributes. However, the majority of genome-visualization occurs either in general-purpose tools such as IGV or the UCSC Genome Browser — which offer many options to adjust visualization parameters, but very little in the way of extensibility — or narrowly-focused tools aiming to solve a single visualization problem. Here, we present genomeview, a python-based visualization engine which is easy to extend and simple to integrate into existing analysis pipelines.

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September 22, 2019

HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads.

Haplotype assembly is the process of assigning the different alleles of the variants covered by mapped sequencing reads to the two haplotypes of the genome of a human individual. Long reads, which are nowadays cheaper to produce and more widely available than ever before, have been used to reduce the fragmentation of the assembled haplotypes since their ability to span several variants along the genome. These long reads are also characterized by a high error rate, an issue which may be mitigated, however, with larger sets of reads, when this error rate is uniform across genome positions. Unfortunately, current state-of-the-art dynamic programming approaches designed for long reads deal only with limited coverages.Here, we propose a new method for assembling haplotypes which combines and extends the features of previous approaches to deal with long reads and higher coverages. In particular, our algorithm is able to dynamically adapt the estimated number of errors at each variant site, while minimizing the total number of error corrections necessary for finding a feasible solution. This allows our method to significantly reduce the required computational resources, allowing to consider datasets composed of higher coverages. The algorithm has been implemented in a freely available tool, HapCHAT: Haplotype Assembly Coverage Handling by Adapting Thresholds. An experimental analysis on sequencing reads with up to 60 × coverage reveals improvements in accuracy and recall achieved by considering a higher coverage with lower runtimes.Our method leverages the long-range information of sequencing reads that allows to obtain assembled haplotypes fragmented in a lower number of unphased haplotype blocks. At the same time, our method is also able to deal with higher coverages to better correct the errors in the original reads and to obtain more accurate haplotypes as a result.HapCHAT is available at http://hapchat.algolab.eu under the GNU Public License (GPL).

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September 22, 2019

A graph-based approach to diploid genome assembly.

Constructing high-quality haplotype-resolved de novo assemblies of diploid genomes is important for revealing the full extent of structural variation and its role in health and disease. Current assembly approaches often collapse the two sequences into one haploid consensus sequence and, therefore, fail to capture the diploid nature of the organism under study. Thus, building an assembler capable of producing accurate and complete diploid assemblies, while being resource-efficient with respect to sequencing costs, is a key challenge to be addressed by the bioinformatics community.We present a novel graph-based approach to diploid assembly, which combines accurate Illumina data and long-read Pacific Biosciences (PacBio) data. We demonstrate the effectiveness of our method on a pseudo-diploid yeast genome and show that we require as little as 50× coverage Illumina data and 10× PacBio data to generate accurate and complete assemblies. Additionally, we show that our approach has the ability to detect and phase structural variants.https://github.com/whatshap/whatshap.Supplementary data are available at Bioinformatics online.

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September 22, 2019

MIRU-profiler: a rapid tool for determination of 24-loci MIRU-VNTR profiles from assembled genomes of Mycobacterium tuberculosis.

Tuberculosis (TB) resulted in an estimated 1.7 million deaths in the year 2016. The disease is caused by the members of Mycobacterium tuberculosis complex, which includes Mycobacterium tuberculosis, Mycobacterium bovis and other closely related TB causing organisms. In order to understand the epidemiological dynamics of TB, national TB control programs often conduct standardized genotyping at 24 Mycobacterial-Interspersed-Repetitive-Units (MIRU)-Variable-Number-of-Tandem-Repeats (VNTR) loci. With the advent of next generation sequencing technology, whole-genome sequencing (WGS) has been widely used for studying TB transmission. However, an open-source software that can connect WGS and MIRU-VNTR typing is currently unavailable, which hinders interlaboratory communication. In this manuscript, we introduce the MIRU-profiler program which could be used for prediction of MIRU-VNTR profile from WGS of M. tuberculosis.The MIRU-profiler is implemented in shell scripting language and depends on EMBOSS software. The in-silico workflow of MIRU-profiler is similar to those described in the laboratory manuals for genotyping M. tuberculosis. Given an input genome sequence, the MIRU-profiler computes alleles at the standard 24-loci based on in-silico PCR amplicon lengths. The final output is a tab-delimited text file detailing the 24-loci MIRU-VNTR pattern of the input sequence.The MIRU-profiler was validated on four datasets: complete genomes from NCBI-GenBank (n = 11), complete genomes for locally isolated strains sequenced using PacBio (n = 4), complete genomes for BCG vaccine strains (n = 2) and draft genomes based on 250 bp paired-end Illumina reads (n = 106).The digital MIRU-VNTR results were identical to the experimental genotyping results for complete genomes of locally isolated strains, BCG vaccine strains and five out of 11 genomes from the NCBI-GenBank. For draft genomes based on short Illumina reads, 21 out of 24 loci were inferred with a high accuracy, while a number of inaccuracies were recorded for three specific loci (ETRA, QUB11b and QUB26). One of the unique features of the MIRU-profiler was its ability to process multiple genomes in a batch. This feature was tested on all complete M. tuberculosis genome (n = 157), for which results were successfully obtained in approximately 14 min.The MIRU-profiler is a rapid tool for inference of digital MIRU-VNTR profile from the assembled genome sequences. The tool can accurately infer repeat numbers at the standard 24 or 21/24 MIRU-VNTR loci from the complete or draft genomes respectively. Thus, the tool is expected to bridge the communication gap between the laboratories using WGS and those using the conventional MIRU-VNTR typing.

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September 22, 2019

Genotype-Corrector: improved genotype calls for genetic mapping in F2 and RIL populations.

F2 and recombinant inbred lines (RILs) populations are very commonly used in plant genetic mapping studies. Although genome-wide genetic markers like single nucleotide polymorphisms (SNPs) can be readily identified by a wide array of methods, accurate genotype calling remains challenging, especially for heterozygous loci and missing data due to low sequencing coverage per individual. Therefore, we developed Genotype-Corrector, a program that corrects genotype calls and imputes missing data to improve the accuracy of genetic mapping. Genotype-Corrector can be applied in a wide variety of genetic mapping studies that are based on low coverage whole genome sequencing (WGS) or Genotyping-by-Sequencing (GBS) related techniques. Our results show that Genotype-Corrector achieves high accuracy when applied to both synthetic and real genotype data. Compared with using raw or only imputed genotype calls, the linkage groups built by corrected genotype data show much less noise and significant distortions can be corrected. Additionally, Genotype-Corrector compares favorably to the popular imputation software LinkImpute and Beagle in both F2 and RIL populations. Genotype-Corrector is publicly available on GitHub at https://github.com/freemao/Genotype-Corrector .

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September 22, 2019

npInv: accurate detection and genotyping of inversions using long read sub-alignment.

Detection of genomic inversions remains challenging. Many existing methods primarily target inzversions with a non repetitive breakpoint, leaving inverted repeat (IR) mediated non-allelic homologous recombination (NAHR) inversions largely unexplored.We present npInv, a novel tool specifically for detecting and genotyping NAHR inversion using long read sub-alignment of long read sequencing data. We benchmark npInv with other tools in both simulation and real data. We use npInv to generate a whole-genome inversion map for NA12878 consisting of 30 NAHR inversions (of which 15 are novel), including all previously known NAHR mediated inversions in NA12878 with flanking IR less than 7kb. Our genotyping accuracy on this dataset was 94%. We used PCR to confirm the presence of two of these novel inversions. We show that there is a near linear relationship between the length of flanking IR and the minimum inversion size, without inverted repeats.The application of npInv shows high accuracy in both simulation and real data. The results give deeper insight into understanding inversion.

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