Evidence map›Paper›PMID 36107768›Full record

ArticleNucleic acids research2022

sgcocaller and comapr: personalised haplotype assembly and comparative crossover map analysis using single-gamete sequencing data.

Ruqian Lyu, Vanessa Tsui, Wayne Crismani, Ruijie Liu, Heejung Shim, Davis J McCarthy

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.6field-weighted citation impact, top 35% of its field
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

6 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Cell genomics · 2023
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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

6 authors at 2 institutions in 1 country.

Ruqian LyuBioinformatics and Cellular Genomics, St Vincent's Institute of Medical Research, 9 Princes Street, Fitzroy, Victoria 3065, Australia.ORCID 0000-0002-7736-6612
Vanessa TsuiDNA Repair and Recombination Laboratory, St Vincent's Institute of Medical Research, 9 Princes Street, Fitzroy, Victoria 3065, Australia.
Wayne CrismaniDNA Repair and Recombination Laboratory, St Vincent's Institute of Medical Research, 9 Princes Street, Fitzroy, Victoria 3065, Australia.ORCID 0000-0003-0143-8293
Ruijie LiuBioinformatics and Cellular Genomics, St Vincent's Institute of Medical Research, 9 Princes Street, Fitzroy, Victoria 3065, Australia.
Heejung ShimMelbourne Integrative Genomics/School of Mathematics and Statistics, Faculty of Science, The University of Melbourne, Building 184, Royal Parade, Parkville, Victoria 3010, Australia.ORCID 0000-0002-1375-9310
Davis J McCarthyBioinformatics and Cellular Genomics, St Vincent's Institute of Medical Research, 9 Princes Street, Fitzroy, Victoria 3065, Australia.ORCID 0000-0002-2218-6833
The University of Melbourne · AUSt Vincents Institute of Medical Research · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Profiling gametes of an individual enables the construction of personalised haplotypes and meiotic crossover landscapes, now achievable at larger scale than ever through the availability of high-throughput single-cell sequencing technologies. However, high-throughput single-gamete data commonly have low depth of coverage per gamete, which challenges existing gamete-based haplotype phasing methods. In addition, haplotyping a large number of single gametes from high-throughput single-cell DNA sequencing data and constructing meiotic crossover profiles using existing methods requires intensive processing. Here, we introduce efficient software tools for the essential tasks of generating personalised haplotypes and calling crossovers in gametes from single-gamete DNA sequencing data (sgcocaller), and constructing, visualising, and comparing individualised crossover landscapes from single gametes (comapr). With additional data pre-possessing, the tools can also be applied to bulk-sequenced samples. We demonstrate that sgcocaller is able to generate impeccable phasing results for high-coverage datasets, on which it is more accurate and stable than existing methods, and also performs well on low-coverage single-gamete sequencing datasets for which current methods fail. Our tools achieve highly accurate results with user-friendly installation, comprehensive documentation, efficient computation times and minimal memory usage.

Indexed as

High-Throughput Nucleotide SequencingPolymorphism, Single NucleotideSequence Analysis, DNAAlgorithmsCrossing Over, GeneticGerm CellsHaplotypesSingle-Cell Gene Expression AnalysisSoftware

Identifiers

PMID36107768
PMCPMC9723612
OpenAlexW4295899404

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.