scholarly journals Deconvolute individual genomes from metagenome sequences through short read clustering

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8966 ◽  
Author(s):  
Kexue Li ◽  
Yakang Lu ◽  
Li Deng ◽  
Lili Wang ◽  
Lizhen Shi ◽  
...  
Keyword(s):  
Large Scale ◽  
False Negative ◽  
Next Generation Sequencing Data ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Short Reads ◽  
Clustering Problem ◽  
Metagenome Assembly ◽  
Real World Datasets ◽  
Almost All

Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality.

scholarly journals Deconvolute individual genomes from metagenome sequences through read clustering

2019 ◽  
Author(s):  
Kexue Li ◽  
Lili Wang ◽  
Lizhen Shi ◽  
Li Deng ◽  
Zhong Wang
Keyword(s):  
Large Scale ◽  
False Negative ◽  
Next Generation Sequencing Data ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Clustering Problem ◽  
Sequencing Coverage ◽  
Metagenome Assembly ◽  
Almost All ◽  
Small Clusters

ABSTRACTMotivationMetagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems.ResultsBased on a previously developed scalable read clustering method on Apache Spark, SpaRC, that has very low false positives, here we extended its capability by adding a new method to further cluster small clusters. This method exploits statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using a synthetic dataset from mouse gut microbiomes we show that this method has the potential to cluster almost all of the reads from genomes with sufficient sequencing coverage. We also explored several clustering parameters that deferentially affect genomes with various sequencing coverage.Availabilityhttps://bitbucket.org/berkeleylab/jgi-sparc/[email protected]

scholarly journals METAMVGL: a multi-view graph-based metagenomic contig binning algorithm by integrating assembly and paired-end graphs

2021 ◽  
Vol 22 (S10) ◽  
Author(s):  
Zhenmiao Zhang ◽  
Lu Zhang
Keyword(s):  
De Novo ◽  
Label Propagation ◽  
Next Generation Sequencing Data ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Fecal Samples ◽  
Microbial Genomes ◽  
Metagenome Assembly ◽  
High Chance ◽  
Mock Communities

Abstract Background Due to the complexity of microbial communities, de novo assembly on next generation sequencing data is commonly unable to produce complete microbial genomes. Metagenome assembly binning becomes an essential step that could group the fragmented contigs into clusters to represent microbial genomes based on contigs’ nucleotide compositions and read depths. These features work well on the long contigs, but are not stable for the short ones. Contigs can be linked by sequence overlap (assembly graph) or by the paired-end reads aligned to them (PE graph), where the linked contigs have high chance to be derived from the same clusters. Results We developed METAMVGL, a multi-view graph-based metagenomic contig binning algorithm by integrating both assembly and PE graphs. It could strikingly rescue the short contigs and correct the binning errors from dead ends. METAMVGL learns the two graphs’ weights automatically and predicts the contig labels in a uniform multi-view label propagation framework. In experiments, we observed METAMVGL made use of significantly more high-confidence edges from the combined graph and linked dead ends to the main graph. It also outperformed many state-of-the-art contig binning algorithms, including MaxBin2, MetaBAT2, MyCC, CONCOCT, SolidBin and GraphBin on the metagenomic sequencing data from simulation, two mock communities and Sharon infant fecal samples. Conclusions Our findings demonstrate METAMVGL outstandingly improves the short contig binning and outperforms the other existing contig binning tools on the metagenomic sequencing data from simulation, mock communities and infant fecal samples.

EdClust: A heuristic sequence clustering method with higher sensitivity

2021 ◽  
Author(s):  
Ming Cao ◽  
Qinke Peng ◽  
Ze-Gang Wei ◽  
Fei Liu ◽  
Yi-Fan Hou
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Clustering Method ◽  
Cluster Number ◽  
Sequence Clustering ◽  
Downstream Analysis ◽  
Heuristic Clustering

The development of high-throughput technologies has produced increasing amounts of sequence data and an increasing need for efficient clustering algorithms that can process massive volumes of sequencing data for downstream analysis. Heuristic clustering methods are widely applied for sequence clustering because of their low computational complexity. Although numerous heuristic clustering methods have been developed, they suffer from two limitations: overestimation of inferred clusters and low clustering sensitivity. To address these issues, we present a new sequence clustering method (edClust) based on Edlib, a C/C[Formula: see text] library for fast, exact semi-global sequence alignment to group similar sequences. The new method edClust was tested on three large-scale sequence databases, and we compared edClust to several classic heuristic clustering methods, such as UCLUST, CD-HIT, and VSEARCH. Evaluations based on the metrics of cluster number and seed sensitivity (SS) demonstrate that edClust can produce fewer clusters than other methods and that its SS is higher than that of other methods. The source codes of edClust are available from https://github.com/zhang134/EdClust.git under the GNU GPL license.

scholarly journals InteractomeSeq: a web server for the identification and profiling of domains and epitopes from phage display and next generation sequencing data

2020 ◽  
Vol 48 (W1) ◽  
pp. W200-W207
Author(s):  
Simone Puccio ◽  
Giorgio Grillo ◽  
Arianna Consiglio ◽  
Maria Felicia Soluri ◽  
Daniele Sblattero ◽  
...  
Keyword(s):  
Phage Display ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Gene Annotation ◽  
Web Server ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Phage Display Technology ◽  
Essential Information ◽  
Research Fields

Abstract High-Throughput Sequencing technologies are transforming many research fields, including the analysis of phage display libraries. The phage display technology coupled with deep sequencing was introduced more than a decade ago and holds the potential to circumvent the traditional laborious picking and testing of individual phage rescued clones. However, from a bioinformatics point of view, the analysis of this kind of data was always performed by adapting tools designed for other purposes, thus not considering the noise background typical of the ‘interactome sequencing’ approach and the heterogeneity of the data. InteractomeSeq is a web server allowing data analysis of protein domains (‘domainome’) or epitopes (‘epitome’) from either Eukaryotic or Prokaryotic genomic phage libraries generated and selected by following an Interactome sequencing approach. InteractomeSeq allows users to upload raw sequencing data and to obtain an accurate characterization of domainome/epitome profiles after setting the parameters required to tune the analysis. The release of this tool is relevant for the scientific and clinical community, because InteractomeSeq will fill an existing gap in the field of large-scale biomarkers profiling, reverse vaccinology, and structural/functional studies, thus contributing essential information for gene annotation or antigen identification. InteractomeSeq is freely available at https://InteractomeSeq.ba.itb.cnr.it/

PERHAPS: Paired-End short Reads-based HAPlotyping from next-generation Sequencing data

2020 ◽  
Author(s):  
Jie Huang ◽  
Stefano Pallotti ◽  
Qianling Zhou ◽  
Marcus Kleber ◽  
Xiaomeng Xin ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Snp Array ◽  
Simple Approach ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Short Read ◽  
Array Data ◽  
Short Reads ◽  
Generation Sequencing

Abstract The identification of rare haplotypes may greatly expand our knowledge in the genetic architecture of both complex and monogenic traits. To this aim, we developed PERHAPS (Paired-End short Reads-based HAPlotyping from next-generation Sequencing data), a new and simple approach to directly call haplotypes from short-read, paired-end Next Generation Sequencing (NGS) data. To benchmark this method, we considered the APOE classic polymorphism (*1/*2/*3/*4), since it represents one of the best examples of functional polymorphism arising from the haplotype combination of two Single Nucleotide Polymorphisms (SNPs). We leveraged the big Whole Exome Sequencing (WES) and SNP-array data obtained from the multi-ethnic UK BioBank (UKBB, N=48,855). By applying PERHAPS, based on piecing together the paired-end reads according to their FASTQ-labels, we extracted the haplotype data, along with their frequencies and the individual diplotype. Concordance rates between WES directly called diplotypes and the ones generated through statistical pre-phasing and imputation of SNP-array data are extremely high (>99%), either when stratifying the sample by SNP-array genotyping batch or self-reported ethnic group. Hardy-Weinberg Equilibrium tests and the comparison of obtained haplotype frequencies with the ones available from the 1000 Genome Project further supported the reliability of PERHAPS. Notably, we were able to determine the existence of the rare APOE*1 haplotype in two unrelated African subjects from UKBB, supporting its presence at appreciable frequency (approximatively 0.5%) in the African Yoruba population. Despite acknowledging some technical shortcomings, PERHAPS represents a novel and simple approach that will partly overcome the limitations in direct haplotype calling from short read-based sequencing.

scholarly journals Binning unassembled short reads based on k-mer abundance covariance using sparse coding

GigaScience ◽  
2020 ◽  
Vol 9 (4) ◽  
Author(s):  
Olexiy Kyrgyzov ◽  
Vincent Prost ◽  
Stéphane Gazut ◽  
Bruno Farcy ◽  
Thomas Brüls
Keyword(s):  
Relative Abundance ◽  
Sparse Coding ◽  
Large Scale ◽  
Computational Cost ◽  
Joint Analysis ◽  
Short Reads ◽  
Elastic Net Regularization ◽  
Sparse Dictionary Learning ◽  
Metagenome Assembly ◽  
Low Levels

Abstract Background Sequence-binning techniques enable the recovery of an increasing number of genomes from complex microbial metagenomes and typically require prior metagenome assembly, incurring the computational cost and drawbacks of the latter, e.g., biases against low-abundance genomes and inability to conveniently assemble multi-terabyte datasets. Results We present here a scalable pre-assembly binning scheme (i.e., operating on unassembled short reads) enabling latent genome recovery by leveraging sparse dictionary learning and elastic-net regularization, and its use to recover hundreds of metagenome-assembled genomes, including very low-abundance genomes, from a joint analysis of microbiomes from the LifeLines DEEP population cohort (n = 1,135, >1010 reads). Conclusion We showed that sparse coding techniques can be leveraged to carry out read-level binning at large scale and that, despite lower genome reconstruction yields compared to assembly-based approaches, bin-first strategies can complement the more widely used assembly-first protocols by targeting distinct genome segregation profiles. Read enrichment levels across 6 orders of magnitude in relative abundance were observed, indicating that the method has the power to recover genomes consistently segregating at low levels.

scholarly journals ASCOT identifies key regulators of neuronal subtype-specific splicing

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jonathan P. Ling ◽  
Christopher Wilks ◽  
Rone Charles ◽  
Patrick J. Leavey ◽  
Devlina Ghosh ◽  
...  
Keyword(s):  
Rna Splicing ◽  
Large Scale ◽  
Splice Variants ◽  
Next Generation Sequencing Data ◽  
Data Sets ◽  
Cell Type ◽  
Sequencing Data ◽  
Large Scale Analysis ◽  
Cell Type Specific ◽  
Public Archives

AbstractPublic archives of next-generation sequencing data are growing exponentially, but the difficulty of marshaling this data has led to its underutilization by scientists. Here, we present ASCOT, a resource that uses annotation-free methods to rapidly analyze and visualize splice variants across tens of thousands of bulk and single-cell data sets in the public archive. To demonstrate the utility of ASCOT, we identify novel cell type-specific alternative exons across the nervous system and leverage ENCODE and GTEx data sets to study the unique splicing of photoreceptors. We find that PTBP1 knockdown and MSI1 and PCBP2 overexpression are sufficient to activate many photoreceptor-specific exons in HepG2 liver cancer cells. This work demonstrates how large-scale analysis of public RNA-Seq data sets can yield key insights into cell type-specific control of RNA splicing and underscores the importance of considering both annotated and unannotated splicing events.

scholarly journals Hidden biases in germline structural variant detection

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Michael M. Khayat ◽  
Sayed Mohammad Ebrahim Sahraeian ◽  
Samantha Zarate ◽  
Andrew Carroll ◽  
Huixiao Hong ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
False Negative ◽  
False Negative Rate ◽  
Next Generation Sequencing Data ◽  
Chinese Family ◽  
Next Generation ◽  
Sequencing Data ◽  
Structural Variations ◽  
The Impact ◽  
Generation Sequencing

Abstract Background Genomic structural variations (SV) are important determinants of genotypic and phenotypic changes in many organisms. However, the detection of SV from next-generation sequencing data remains challenging. Results In this study, DNA from a Chinese family quartet is sequenced at three different sequencing centers in triplicate. A total of 288 derivative data sets are generated utilizing different analysis pipelines and compared to identify sources of analytical variability. Mapping methods provide the major contribution to variability, followed by sequencing centers and replicates. Interestingly, SV supported by only one center or replicate often represent true positives with 47.02% and 45.44% overlapping the long-read SV call set, respectively. This is consistent with an overall higher false negative rate for SV calling in centers and replicates compared to mappers (15.72%). Finally, we observe that the SV calling variability also persists in a genotyping approach, indicating the impact of the underlying sequencing and preparation approaches. Conclusions This study provides the first detailed insights into the sources of variability in SV identification from next-generation sequencing and highlights remaining challenges in SV calling for large cohorts. We further give recommendations on how to reduce SV calling variability and the choice of alignment methodology.

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