Evidence map›Paper›PMID 37732227›Full record

ArticlebioRxiv : the preprint server for biology2023

Xenomake: a pipeline for processing and sorting xenograft reads from spatial transcriptomic experiments.

Benjamin S Strope, Katherine E Pendleton, William Z Bowie, Gloria V Echeverria, Qian Zhu

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Benjamin S StropeLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.
Katherine E PendletonLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.
William Z BowieLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.
Gloria V EcheverriaLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.
Qian ZhuLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.

Funding

Tumor BiologyP30CA125123 · NCI · BAYLOR COLLEGE OF MEDICINE · PI Suzanne AW Fuqua · 2007 to 2026
$73.9M
Metabolic adaptation in residual triple negative breast cancer following chemotherapyR37CA269783 · NCI · BAYLOR COLLEGE OF MEDICINE · PI Gloria Vittone Echeverria · 2023 to 2026
$2.1M
High Throughput Genomic Sequencer at BCM Core FacilityS10OD023469 · OD · BAYLOR COLLEGE OF MEDICINE · PI CHEN, RUI · 2017 to 2017
$600k
Investigating the role of CRAT as a driver of triple negative breast cancer chemoresistanceF31CA275397 · NCI · BAYLOR COLLEGE OF MEDICINE · PI PENDLETON, KATHERINE ELLEN · 2022 to 2024
$143k
NCI NIH HHS F31 CA275397NCI NIH HHS P30 CA125123NCI NIH HHS R37 CA269783NIH HHS S10 OD023469
6 · The paper itself

Abstract

Xenograft models are attractive models that mimic human tumor biology and permit one to perturb the tumor microenvironment and study its drug response. Spatially resolved transcriptomics (SRT) provide a powerful way to study the organization of xenograft models, but currently there is a lack of specialized pipeline for processing xenograft reads originated from SRT experiments. Xenomake is a standalone pipeline for the automated handling of spatial xenograft reads. Xenomake handles read processing, alignment, xenograft read sorting, quantification, and connects well with downstream spatial analysis packages. We additionally show that Xenomake can correctly assign organism specific reads, reduce sparsity of data by increasing gene counts, while maintaining biological relevance for studies.

Identifiers

PMID37732227
PMCPMC10508769

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.