Evidence map›Paper›PMID 39786867›Full record

ArticleBioinformatics (Oxford, England)2024

Semblans: automated assembly and processing of RNA-seq data.

Miles D Woodcock-Girard, Eric C Bretz, Holly M Robertson, Karolis Ramanauskas, Jarrad T Hampton-Marcell, Joseph F Walker

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Miles D Woodcock-GirardDepartment of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, United States.
Eric C BretzDepartment of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, United States.
Holly M RobertsonThe Sainsbury Laboratory, University of Cambridge, Cambridge, CB2 1LR, United Kingdom.
Karolis RamanauskasDepartment of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, United States.
Jarrad T Hampton-MarcellDepartment of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, United States.ORCID 0000-0003-1982-4416
Joseph F WalkerDepartment of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, United States.ORCID 0000-0003-2928-8899

Funding

Gatsby Charitable Foundation PTAG/022
6 · The paper itself

Abstract

motivationRecent advancements in parallel sequencing methods have precipitated a surge in publicly available short-read sequence data. This has encouraged the development of novel computational tools for the de novo assembly of transcriptomes from RNA-seq data. Despite the availability of these tools, performing an end-to-end transcriptome assembly remains a programmatically involved task necessitating familiarity with best practices. Aside from quality control steps, including error correction, adapter trimming, and chimera filtration needing to be correctly used, moving data between programs often requires manual reformatting or restructuring, which can further impede throughput. Here, we introduce Semblans, a tool for streamlining the assembly process that efficiently and consistently produces high-quality transcriptome assemblies.

resultsSemblans abstracts the key quality control, reconstitution, and postprocessing steps of transcriptome assembly from raw short-read sequences to annotated coding sequences. Evaluating its performance against previously assembled transcriptomes on the basis of assembly quality, we find that Semblans produced higher quality assemblies for 98 of the 101 short-read runs tested. AVAILABILITY AND IMPLEMENTATION: Semblans is written in C++ and runs on Unix-compliant operating systems. Source code, documentation, and compiled binaries are hosted under the GNU General Public License at https://github.com/gladshire/Semblans.

Indexed as

RNA-SeqSequence Analysis, RNASoftwareComputational BiologyGene Expression ProfilingHigh-Throughput Nucleotide SequencingTranscriptome

Identifiers

PMID39786867
PMCPMC11748423

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.