Improving in-silico normalization using read weights

Durai, Dilip Ariyur; Schulz, Marcel H.

doi:10.1038/s41598-019-41502-9

Cited by 13 publications

(16 citation statements)

References 26 publications

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“…The outcome is a strong reduction of the read volume in such a manner that full length reconstruction of a large majority of the transcript cohort can be achieved despite fewer reads being input to the assembler [ 45 , 46 ]. Some tools that can perform in silico read normalization include (using the algorithm) [ 47 ], [ 48 ], [ 49 ] and [ 50 ]. The [ 46 ] assembler also offers in-built in silico normalization [ 45 , 46 ].…”

Section: Pre-assembly Quality Control and Filteringmentioning

confidence: 99%

A simple guide to de novo transcriptome assembly and annotation

Raghavan

Kraft

Mesny

et al. 2022

Briefings in Bioinformatics

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A transcriptome constructed from short-read RNA sequencing (RNA-seq) is an easily attainable proxy catalog of protein-coding genes when genome assembly is unnecessary, expensive or difficult. In the absence of a sequenced genome to guide the reconstruction process, the transcriptome must be assembled de novo using only the information available in the RNA-seq reads. Subsequently, the sequences must be annotated in order to identify sequence-intrinsic and evolutionary features in them (for example, protein-coding regions). Although straightforward at first glance, de novo transcriptome assembly and annotation can quickly prove to be challenging undertakings. In addition to familiarizing themselves with the conceptual and technical intricacies of the tasks at hand and the numerous pre- and post-processing steps involved, those interested must also grapple with an overwhelmingly large choice of tools. The lack of standardized workflows, fast pace of development of new tools and techniques and paucity of authoritative literature have served to exacerbate the difficulty of the task even further. Here, we present a comprehensive overview of de novo transcriptome assembly and annotation. We discuss the procedures involved, including pre- and post-processing steps, and present a compendium of corresponding tools.

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Section: Pre-assembly Quality Control and Filteringmentioning

confidence: 99%

A simple guide to de novo transcriptome assembly and annotation

Raghavan

Kraft

Mesny

et al. 2022

Briefings in Bioinformatics

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“…Quality controlled reads were error corrected and digitally normalized to a target coverage of 100× using BBnorm (BBtools suite). Digital normalization was performed to remove redundant reads, thus reducing memory requirements on the assembly step [27,28]. The k-mer abundances on the sequencing reads were calculated, and reads with an estimated mean coverage over a user-defined threshold were rejected.…”

Section: Quality Control and Assemblymentioning

confidence: 99%

Bacteriophages Roam the Wheat Phyllosphere

Forero-Junco

Alanin

Djurhuus

et al. 2022

Viruses

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The phyllosphere microbiome plays an important role in plant fitness. Recently, bacteriophages have been shown to play a role in shaping the bacterial community composition of the phyllosphere. However, no studies on the diversity and abundance of phyllosphere bacteriophage communities have been carried out until now. In this study, we extracted, sequenced, and characterized the dsDNA and ssDNA viral community from a phyllosphere for the first time. We sampled leaves from winter wheat (Triticum aestivum), where we identified a total of 876 virus operational taxonomic units (vOTUs), mostly predicted to be bacteriophages with a lytic lifestyle. Remarkably, 848 of these vOTUs corresponded to new viral species, and we estimated a minimum of 2.0 × 106 viral particles per leaf. These results suggest that the wheat phyllosphere harbors a large and active community of novel bacterial viruses. Phylloviruses have potential applications as biocontrol agents against phytopathogenic bacteria or as microbiome modulators to increase plant growth-promoting bacteria.

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“…Paired-end reads were de novo assembled using fq2dna version 21.06 ( https://gitlab.pasteur.fr/GIPhy/fq2dna; strategy B; default settings). The corresponding fq2dna pipeline consists of trimming and clipping of low-quality reads and adapters with AlienTrimmer (version 2.0) [ 77 ], sequencing error correction with Musket (version 1.1) [ 78 ], paired-end read merging with FLASh (version 1.2.11) [ 79 ], coverage homogenization with ROCK (version 1.9.3; https://gitlab.pasteur.fr/vlegrand/ROCK ) [ 81 , 82 , 90 ], and de novo assembly with SPAdes (version 3.15.0) [ 57 ]. In brief, the paired-end reads were first pre-processed through deduplication, clipping, trimming (Phred score threshold: 15, minimum read length: 50 bp) and error correction.…”

Section: Methodsmentioning

confidence: 99%

In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

et al. 2022

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Background Whole genome sequencing analyzed by core genome multi-locus sequence typing (cgMLST) is widely used in surveillance of the pathogenic bacteria Listeria monocytogenes. Given the heterogeneity of available bioinformatics tools to define cgMLST alleles, our aim was to identify parameters influencing the precision of cgMLST profiles. Methods We used three L. monocytogenes reference genomes from different phylogenetic lineages and assessed the impact of in vitro (i.e. tested genomes, successive platings, replicates of DNA extraction and sequencing) and in silico parameters (i.e. targeted depth of coverage, depth of coverage, breadth of coverage, assembly metrics, cgMLST workflows, cgMLST completeness) on cgMLST precision made of 1748 core loci. Six cgMLST workflows were tested, comprising assembly-based (BIGSdb, INNUENDO, GENPAT, SeqSphere and BioNumerics) and assembly-free (i.e. kmer-based MentaLiST) allele callers. Principal component analyses and generalized linear models were used to identify the most impactful parameters on cgMLST precision. Results The isolate’s genetic background, cgMLST workflows, cgMLST completeness, as well as depth and breadth of coverage were the parameters that impacted most on cgMLST precision (i.e. identical alleles against reference circular genomes). All workflows performed well at ≥40X of depth of coverage, with high loci detection (> 99.54% for all, except for BioNumerics with 97.78%) and showed consistent cluster definitions using the reference cut-off of ≤7 allele differences. Conclusions This highlights that bioinformatics workflows dedicated to cgMLST allele calling are largely robust when paired-end reads are of high quality and when the sequencing depth is ≥40X.

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Improving in-silico normalization using read weights

Cited by 13 publications

References 26 publications

A simple guide to de novo transcriptome assembly and annotation

A simple guide to de novo transcriptome assembly and annotation

Bacteriophages Roam the Wheat Phyllosphere

In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

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