ArticleBioinformatics advances2026
pycmplot: fast, low-memory multi-track circular and linear Manhattan plotting in Python.
Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Manhattan plots remain the standard visual summary for genome-wide association studies and other per-variant scans (iHS, FST, XP-EHH). Existing Manhattan visualization tools either lack multi-track support (gwaslab, qmplot, and qqman) or do not provide a Circos-style multi-track circular Manhattan view. CMplot (R) does, but its memory and runtime costs on multi-million-variant inputs are substantial. Results: We present pycmplot, a Python package for generating multi-track linear and Circos-style circular Manhattan plots from one or more GWAS summary statistics. On a 10-million-variant scan, pycmplot is 5.5× faster and uses ∼5× less memory than CMplot for linear Manhattan plotting, 1.6× faster (and 3× faster with optional pre-filtering) for circular Manhattan, and 7.7× faster for qq rendering. Multi-track comparisons of two summary statistics are 4.5× faster (linear) and 3.5× faster (circular) than CMplot's equivalent mode. Transparent hg18/hg19 → hg38 liftover lets users mix coordinate systems within a single plot, and a lightweight bundle of Ensembl GFF3 gene information files (hg19 and hg38) enables nearest gene annotation. Availability and implementation: pycmplot is freely available under the MIT license at https://github.com/esohkevin/pycmplot; and from PyPI (pip install pycmplot). Documentation is at https://pycmplot.readthedocs.io.
Identifiers
What OpenQuestion holds
Registered trials
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.