Evidence map›Paper›PMID 39974956›Full record

ArticlebioRxiv : the preprint server for biology2025

IGD: A simple, efficient genotype data format.

Drew DeHaas, Xinzhu Wei

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

2 authors.

Drew DeHaasDepartment of Computational Biology, Cornell University, Ithaca, NY.ORCID 0009-0005-9087-1831
Xinzhu WeiDepartment of Computational Biology, Cornell University, Ithaca, NY.ORCID 0000-0001-8184-7016

Funding

Dissecting the genetics and evolution of complex traits using whole-genome genealogiesR35GM150579 · NIGMS · CORNELL UNIVERSITY · PI Xinzhu Wei · 2023 to 2026
$1.5M
NIGMS NIH HHS R35 GM150579
6 · The paper itself

Abstract

Motivation: While there are a variety of file formats for storing reference-sequence-aligned genotype data, many are complex or inefficient. Programming language support for such formats is often limited. A file format that is simple to understand and implement - yet fast and small - is helpful for research on highly scalable bioinformatics. Results: We present the Indexable Genotype Data (IGD) file format, a simple uncompressed binary format that can be more than 100 times faster and 3.5 times smaller than Availability: A C++ library reading and writing IGD, and tooling to convert

Identifiers

PMID39974956
PMCPMC11838554

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.