CStone: A de novo transcriptome assembler for short-read data that identifies non-chimeric contigs based on underlying graph structure

Linheiro, Raquel; Archer, John

doi:10.1371/journal.pcbi.1009631

Cited by 5 publications

(3 citation statements)

References 68 publications

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“…Given that the amount of DNA involved in these studies is minimal, MDA is applied to amplify the DNA for subsequent sequencing, but the chimerism generated during the process can greatly impact the overall accuracy. Researches indicate that the chimerism in MDA sequencing data cannot be ignored and is attracting increasing attention, especially as single-cell studies have become a hot topic [13] , [51] , [52] , [53] , [54] .…”

Section: Introductionmentioning

confidence: 99%

Chimera: The spoiler in multiple displacement amplification

Lü¹,

Qiao²,

Zhang³

et al. 2023

Computational and Structural Biotechnology Journal

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Section: Introductionmentioning

confidence: 99%

Chimera: The spoiler in multiple displacement amplification

Lü¹,

Qiao²,

Zhang³

et al. 2023

Computational and Structural Biotechnology Journal

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“…Background count variation refers to varying count levels associated with individual transcripts that are not maintained across replicates of a condition, thus effectively reflecting intra-condition noise, whilst specifying a subset of transcripts to be over represented across replicates of a condition reflects identifiable over expressed transcripts. A similar experiment to those described here, but in relation to the effects of chimerism on the results of differential expression, has been described in Linheiro and Archer (2021) [ 50 ].…”

Section: Methodsmentioning

confidence: 60%

“…High intra-condition count variation at an inter, and to a lesser extent intra, study level can arise from a range of sources including i) biological differences between samples such as age, sex, diet, and health; ii) in silica error involving assembly tools producing poorly understood chimeras within the reference transcriptome [ 50 , 60 , 61 ]; iii) ambiguities in read mapping to such references [ 62 ]; iv) normalization of count data derived from such mapped reads [ 63 ]; and v) including in vitro error during library preparation protocols [ 64 , 65 ]. Although we used DESeq2 within our study, the results of our exploration on the effects of intra-condition variation in the detection of differentially expressed transcripts likely applies to other software used for differential expression analysis that rely on per transcript count information across replicates for the estimation of transcript abundance and dispersion, for example, edgeR [ 10 ], BBSeq [ 66 ], DSS [ 67 ], baySeq [ 68 ] and ShrinkBayes [ 69 ].…”

Section: Discussionmentioning

confidence: 99%

On taming the effect of transcript level intra-condition count variation during differential expression analysis: A story of dogs, foxes and wolves

et al. 2022

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The evolution of RNA-seq technologies has yielded datasets of scientific value that are often generated as condition associated biological replicates within expression studies. With expanding data archives opportunity arises to augment replicate numbers when conditions of interest overlap. Despite correction procedures for estimating transcript abundance, a source of ambiguity is transcript level intra-condition count variation; as indicated by disjointed results between analysis tools. We present TVscript, a tool that removes reference-based transcripts associated with intra-condition count variation above specified thresholds and we explore the effects of such variation on differential expression analysis. Initially iterative differential expression analysis involving simulated counts, where levels of intra-condition variation and sets of over represented transcripts are explicitly specified, was performed. Then counts derived from inter- and intra-study data representing brain samples of dogs, wolves and foxes (wolves vs. dogs and aggressive vs. tame foxes) were used. For simulations, the sensitivity in detecting differentially expressed transcripts increased after removing hyper-variable transcripts, although at levels of intra-condition variation above 5% detection became unreliable. For real data, prior to applying TVscript, ≈20% of the transcripts identified as being differentially expressed were associated with high levels of intra-condition variation, an over representation relative to the reference set. As transcripts harbouring such variation were removed pre-analysis, a discordance from 26 to 40% in the lists of differentially expressed transcripts is observed when compared to those obtained using the non-filtered reference. The removal of transcripts possessing intra-condition variation values within (and above) the 97th and 95th percentiles, for wolves vs. dogs and aggressive vs. tame foxes, maximized the sensitivity in detecting differentially expressed transcripts as a result of alterations within gene-wise dispersion estimates. Through analysis of our real data the support for seven genes with potential for being involved with selection for tameness is provided. TVscript is available at: https://sourceforge.net/projects/tvscript/.

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Exploration of whole genome amplification generated chimeric sequences in long-read sequencing data

Lu,

Qiao,

et al. 2023

Briefings in Bioinformatics

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Motivation Multiple displacement amplification (MDA) has become the most commonly used method of whole genome amplification, generating a vast amount of DNA with higher molecular weight and greater genome coverage. Coupling with long-read sequencing, it is possible to sequence the amplicons of over 20 kb in length. However, the formation of chimeric sequences (chimeras, expressed as structural errors in sequencing data) in MDA seriously interferes with the bioinformatics analysis but its influence on long-read sequencing data is unknown. Results We sequenced the phi29 DNA polymerase-mediated MDA amplicons on the PacBio platform and analyzed chimeras within the generated data. The 3rd-ChimeraMiner has been constructed as a pipeline for recognizing and restoring chimeras into the original structures in long-read sequencing data, improving the efficiency of using TGS data. Five long-read datasets and one high-fidelity long-read dataset with various amplification folds were analyzed. The result reveals that the mis-priming events in amplification are more frequently occurring than widely perceived, and the propor tion gradually accumulates from 42% to over 78% as the amplification continues. In total, 99.92% of recognized chimeric sequences were demonstrated to be artifacts, whose structures were wrongly formed in MDA instead of existing in original genomes. By restoring chimeras to their original structures, the vast majority of supplementary alignments that introduce false-positive structural variants are recycled, removing 97% of inversions on average and contributing to the analysis of structural variation in MDA-amplified samples. The impact of chimeras in long-read sequencing data analysis should be emphasized, and the 3rd-ChimeraMiner can help to quantify and reduce the influence of chimeras. Availability and implementation The 3rd-ChimeraMiner is available on GitHub, https://github.com/dulunar/3rdChimeraMiner.

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CStone: A de novo transcriptome assembler for short-read data that identifies non-chimeric contigs based on underlying graph structure

Cited by 5 publications

References 68 publications

Chimera: The spoiler in multiple displacement amplification

Chimera: The spoiler in multiple displacement amplification

On taming the effect of transcript level intra-condition count variation during differential expression analysis: A story of dogs, foxes and wolves

Exploration of whole genome amplification generated chimeric sequences in long-read sequencing data

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