Evidence map›Paper›PMID 32693839›Full record

ArticleBiology of sex differences2020

Reference genome and transcriptome informed by the sex chromosome complement of the sample increase ability to detect sex differences in gene expression from RNA-Seq data.

Kimberly C Olney, Sarah M Brotman, Jocelyn P Andrews, Valeria A Valverde-Vesling, Melissa A Wilson

Open access · goldAbstract read
In one paragraph

Article in Biology of sex differences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 1 pooled it
6.1field-weighted citation impact, top 3% of its field
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

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

37 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.

  1. Pooled it
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  6. Observational
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  19. Aneuploidy effects on human gene expression across three cell types.Proceedings of the National Academy of Sciences of the United States of America · 2023
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Kimberly C OlneySchool of Life Sciences, Arizona State University, PO Box 874501, Tempe, AZ, 85287-4501, USA.
Sarah M BrotmanSchool of Life Sciences, Arizona State University, PO Box 874501, Tempe, AZ, 85287-4501, USA.
Jocelyn P AndrewsSchool of Life Sciences, Arizona State University, PO Box 874501, Tempe, AZ, 85287-4501, USA.
Valeria A Valverde-VeslingSchool of Life Sciences, Arizona State University, PO Box 874501, Tempe, AZ, 85287-4501, USA.
Melissa A WilsonSchool of Life Sciences, Arizona State University, PO Box 874501, Tempe, AZ, 85287-4501, USA. mwilsons@asu.edu.ORCID 0000-0002-2614-0285
Arizona State University · USUniversity of North Carolina at Chapel Hill · US

Funding

Population dynamics and medical consequences of sex chromosome evolutionR35GM124827 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI WILSON, MELISSA A · 2017 to 2024
$2.7M
Biodesign Institute, Arizona State University (US) StartupNational Institute of General Medical Sciences (US) R35GM124827NIGMS NIH HHS R35 GM124827School of Life Sciences, Arizona State University (US) Startup
6 · The paper itself

Abstract

backgroundHuman X and Y chromosomes share an evolutionary origin and, as a consequence, sequence similarity. We investigated whether the sequence homology between the X and Y chromosomes affects the alignment of RNA-Seq reads and estimates of differential expression. We tested the effects of using reference genomes and reference transcriptomes informed by the sex chromosome complement of the sample's genome on the measurements of RNA-Seq abundance and sex differences in expression.

resultsThe default genome includes the entire human reference genome (GRCh38), including the entire sequence of the X and Y chromosomes. We created two sex chromosome complement informed reference genomes. One sex chromosome complement informed reference genome was used for samples that lacked a Y chromosome; for this reference genome version, we hard-masked the entire Y chromosome. For the other sex chromosome complement informed reference genome, to be used for samples with a Y chromosome, we hard-masked only the pseudoautosomal regions of the Y chromosome, because these regions are duplicated identically in the reference genome on the X chromosome. We analyzed the transcript abundance in the whole blood, brain cortex, breast, liver, and thyroid tissues from 20 genetic female (46, XX) and 20 genetic male (46, XY) samples. Each sample was aligned twice: once to the default reference genome and then independently aligned to a reference genome informed by the sex chromosome complement of the sample, repeated using two different read aligners, HISAT and STAR. We then quantified sex differences in gene expression using featureCounts to get the raw count estimates followed by Limma/Voom for normalization and differential expression. We additionally created sex chromosome complement informed transcriptome references for use in pseudo-alignment using Salmon. Transcript abundance was quantified twice for each sample: once to the default target transcripts and then independently to target transcripts informed by the sex chromosome complement of the sample.

conclusionsWe show that regardless of the choice of the read aligner, using an alignment protocol informed by the sex chromosome complement of the sample results in higher expression estimates on the pseudoautosomal regions of the X chromosome in both genetic male and genetic female samples, as well as an increased number of unique genes being called as differentially expressed between the sexes. We additionally show that using a pseudo-alignment approach informed on the sex chromosome complement of the sample eliminates Y-linked expression in female XX samples.

Indexed as

Genome, HumanRNA-SeqTranscriptomeChromosomes, Human, XChromosomes, Human, YFemaleGene Expression RegulationHumansMaleAlignmentDifferential expressionMappingPseudo-alignmentQuantificationRNA-SeqSex chromosomesTranscriptome

Identifiers

PMID32693839
PMCPMC7374973
OpenAlexW3044114531

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.