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Hello, I was wondering with IDBA-Tran they want you to have a .fasta file as input. I converted my file from .fastq to .fasta but I noticed in their example conversion they also filter out reads with NNNs in them. How important is this for proper assembly of the transcriptome? Thanks.
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It is always an option to clean your reads of NNN and other low quality reads, but you don't need to, for this and other assemblers. De-novo assemblers do internally use higher quality reads instead of poor ones where available. I do both ways sometimes, but current Illumina sequencer outputs are generally high quality.
I get good transcript assemblies with idba-trans, with a caveat that I use all the kmer-set "transcripts-kk.fa" outputs, rather than IDBA's final "merged" set, which has poorer gene completeness than the per-kmer sets.
Find here a comparison of 4 gene assemblers, used in EvidentialGene's production of highly accurate gene reconstructions for the malaria mosquito and yellow fever/Zika mosquito:
idba_trans comes in second best, after velvet/oases the reigning champion of gene assembly. idba_trans uses less memory and runs faster than velvet/oases, and the authors' paper helps explain why it and velvet/oases do so well with multi-kmer constructions of the variably expressed genes and their alternate transcripts. Please consider using http://eugenes.org/EvidentialGene/ methods along with several gene assemblers to obtain highly accurate animal and plant gene sets.
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...Today, 07:11 AM -
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
...06-02-2026, 10:05 AM -
With the launch of new single-cell sequencing platforms in 2026, the field stands at an exciting inflection point. This article surveys the most impactful advances in the field and discusses how they’re reshaping research in cancer, immunology, and beyond.
Introduction
Single-cell sequencing technologies have undergone remarkable advances over the past decade, transitioning from low-throughput experimental approaches to highly scalable platforms capable of...05-22-2026, 06:42 AM
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