Evidence map›Paper›PMID 40331170›Full record

ArticleImmunoinformatics (Amsterdam, Netherlands)2024

Scifer: An R/Bioconductor package for large-scale integration of Sanger sequencing and flow cytometry data of index-sorted single cells.

Rodrigo Arcoverde Cerveira, Klara Lenart, Marcel Martin, Matthew James Hinchcliff, Fredrika Hellgren, Kewei Ye, Juliana Assis Geraldo, Taras Kreslavsky, Sebastian Ols, Karin Loré

Abstract read
In one paragraph

Article in Immunoinformatics (Amsterdam, Netherlands), 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

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

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

10 authors.

Rodrigo Arcoverde CerveiraDivision of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Center for Molecular Medicine (CMM), Karolinska Institutet, Visionsgatan 18, Stockholm 171 64, Sweden.
Klara LenartDivision of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Center for Molecular Medicine (CMM), Karolinska Institutet, Visionsgatan 18, Stockholm 171 64, Sweden.
Marcel MartinDept of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University, Box 1031, Solna SE-17121, Sweden.
Matthew James HinchcliffDivision of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Center for Molecular Medicine (CMM), Karolinska Institutet, Visionsgatan 18, Stockholm 171 64, Sweden.
Fredrika HellgrenDivision of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Center for Molecular Medicine (CMM), Karolinska Institutet, Visionsgatan 18, Stockholm 171 64, Sweden.
Kewei YeDivision of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Center for Molecular Medicine (CMM), Karolinska Institutet, Visionsgatan 18, Stockholm 171 64, Sweden.
Juliana Assis GeraldoDepartment of Immunotechnology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Lund University, Lund SE-221 00, Sweden.
Taras KreslavskyDivision of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Center for Molecular Medicine (CMM), Karolinska Institutet, Visionsgatan 18, Stockholm 171 64, Sweden.
Sebastian OlsDivision of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Center for Molecular Medicine (CMM), Karolinska Institutet, Visionsgatan 18, Stockholm 171 64, Sweden.
Karin LoréDivision of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Center for Molecular Medicine (CMM), Karolinska Institutet, Visionsgatan 18, Stockholm 171 64, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sanger sequencing remains widely used in various experimental contexts, often in combination with flow cytometry for indexing specific cell populations. However, existing software lacks the capability to automate quality control (QC) of raw Sanger sequencing data and integrate it with flow cytometry information on a large scale. Here, we introduce scifer, an R package now available in the latest release of Bioconductor (3.20) showcasing its effectiveness in seamlessly integrating these types of data as demonstrated by analyses of B cell and T cell receptor sequences. Scifer preprocesses raw data from index sorts and immune receptor Sanger sequencing. It identifies high-quality sequences based on selected parameters, such as length, Phred scores, and heavy-chain complementarity-determining region 3 (HCDR3) quality. As a result, the quality of germline assignments is significantly increased and spurious variable gene mutations are reduced. Scifer is automated and can process thousands of sequences in less than an hour. Its output provides quality control reports, FASTA files, summarized tables, and electropherograms for manual inspection. In summary, scifer is a user-friendly software that speeds up the analysis of immune receptor repertoire sequences, offering wide applicability.

Indexed as

AIRRAntibodiesB cell receptorBioconductorData integrationFlow cytometryQuality controlR packageSanger sequencingT cell receptor

Identifiers

PMID40331170
PMCPMC12052378

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Registered trials

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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.