Vanja
@halfacrocodile.bsky.social
bald but bearded bioinformatics buddy👨🦲
https://autosome.org
https://autosome.org
(15/15) MIXALIME is written in Python, getting the stable version is as easy as 'pip install mixalime', and the source code plus tutorial are freely available at github.com/autosome-ru/...
February 19, 2025 at 11:43 AM
(15/15) MIXALIME is written in Python, getting the stable version is as easy as 'pip install mixalime', and the source code plus tutorial are freely available at github.com/autosome-ru/...
(13/15) According to stratified LD score regression, these rSNPs correspond to regulatory regions involved in cell type-specific phenotypes. Most importantly, the collection of significant rSNPs can be fully explored at udacha.autosome.org
February 19, 2025 at 11:43 AM
(13/15) According to stratified LD score regression, these rSNPs correspond to regulatory regions involved in cell type-specific phenotypes. Most importantly, the collection of significant rSNPs can be fully explored at udacha.autosome.org
(12/15) AS-chromatin variants are predominantly located in promoter and enhancer regions and significantly overlap ADASTRA ASBs and GTEx eQTLs.
February 19, 2025 at 11:43 AM
(12/15) AS-chromatin variants are predominantly located in promoter and enhancer regions and significantly overlap ADASTRA ASBs and GTEx eQTLs.
(11/15) Finally, we used MIXALIME to analyze 5858 chromatin accessibility datasets from gtrd.biouml.org. In the end, we identified >200 thousand allele-specific chromatin accessibility variants.
February 19, 2025 at 11:43 AM
(11/15) Finally, we used MIXALIME to analyze 5858 chromatin accessibility datasets from gtrd.biouml.org. In the end, we identified >200 thousand allele-specific chromatin accessibility variants.
(10/15) In most cases, MIXALIME outperforms other popular tools for AS calling, offering a good sensitivity/specificity tradeoff.
February 19, 2025 at 11:43 AM
(10/15) In most cases, MIXALIME outperforms other popular tools for AS calling, offering a good sensitivity/specificity tradeoff.
(8/15) Copy-number variation and aneuploidy are accounted for by fitting a mixture model assuming that reads originate from haplotypes with different copy numbers.
February 19, 2025 at 11:43 AM
(8/15) Copy-number variation and aneuploidy are accounted for by fitting a mixture model assuming that reads originate from haplotypes with different copy numbers.
(7/15) MIXALIME also handles reference mapping bias and aneuploidy, see the underlying math in arxiv.org/abs/2306.08287. To counter the mapping bias, MIXALIME uses separate fits for Alt read counts with the fixed number of Ref reads and vice versa.
February 19, 2025 at 11:43 AM
(7/15) MIXALIME also handles reference mapping bias and aneuploidy, see the underlying math in arxiv.org/abs/2306.08287. To counter the mapping bias, MIXALIME uses separate fits for Alt read counts with the fixed number of Ref reads and vice versa.
(6/15) MIXALIME provides a variety of statistical models to fulfill particular use cases, from a standard binomial model to the beta negative binomial (BetaNB) model that accounts for extra overdispersion.
February 19, 2025 at 11:43 AM
(6/15) MIXALIME provides a variety of statistical models to fulfill particular use cases, from a standard binomial model to the beta negative binomial (BetaNB) model that accounts for extra overdispersion.
(5/15) MIXALIME is a novel toolbox that uses MIXture models for ALlelic IMbalance Estimation. In the paper, we describe a general workflow from FASTQ files to allelic read counts and SNV-level allele-specific statistics.
February 19, 2025 at 11:43 AM
(5/15) MIXALIME is a novel toolbox that uses MIXture models for ALlelic IMbalance Estimation. In the paper, we describe a general workflow from FASTQ files to allelic read counts and SNV-level allele-specific statistics.
(4/15) Technically, the allele specificity is revealed by counting the number of reads supporting each of the alleles and estimating the statistical significance of the observed allelic imbalance.
February 19, 2025 at 11:43 AM
(4/15) Technically, the allele specificity is revealed by counting the number of reads supporting each of the alleles and estimating the statistical significance of the observed allelic imbalance.
(3/15) High-throughput sequencing allows tracking chromatin state, gene expression, protein-DNA interactions, and more. Eventually, all methods yield short reads that can be used to call single-nucleotide variants and assess the allele specificity.
February 19, 2025 at 11:43 AM
(3/15) High-throughput sequencing allows tracking chromatin state, gene expression, protein-DNA interactions, and more. Eventually, all methods yield short reads that can be used to call single-nucleotide variants and assess the allele specificity.
(1/15) Yet another sweet bioinformatics "software+database" couple from our team:
Meet MIXALIME, a framework for assessing allelic imbalance, and UDACHA, a database of allele-specific chromatin accessibility, read more at www.nature.com/articles/s41...
Meet MIXALIME, a framework for assessing allelic imbalance, and UDACHA, a database of allele-specific chromatin accessibility, read more at www.nature.com/articles/s41...
February 19, 2025 at 11:43 AM
(1/15) Yet another sweet bioinformatics "software+database" couple from our team:
Meet MIXALIME, a framework for assessing allelic imbalance, and UDACHA, a database of allele-specific chromatin accessibility, read more at www.nature.com/articles/s41...
Meet MIXALIME, a framework for assessing allelic imbalance, and UDACHA, a database of allele-specific chromatin accessibility, read more at www.nature.com/articles/s41...
(4/4) Don't hesitate to grab a fresh release from hocomoco.autosome.org and remember that we also provide a fancy online motif scanner, MoLoTool, in all its interactive JS-powered beauty.
February 17, 2025 at 11:38 AM
(4/4) Don't hesitate to grab a fresh release from hocomoco.autosome.org and remember that we also provide a fancy online motif scanner, MoLoTool, in all its interactive JS-powered beauty.
(2/4) v13 covers >1100 of ~1600 human TFs with >1600 primary motifs and subtypes. Since v12 we also provide a reduced non-redundant set of motifs, which are often shared between TFs with similar DBDs.
February 17, 2025 at 11:38 AM
(2/4) v13 covers >1100 of ~1600 human TFs with >1600 primary motifs and subtypes. Since v12 we also provide a reduced non-redundant set of motifs, which are often shared between TFs with similar DBDs.
(1/4) Thrilled to announce another major release of the HOCOMOCO motif collection, well-known for its silly name and rigorous approach to constructing and benchmarking DNA sequence motifs recognized by human and mouse transcription factors.
hocomoco.autosome.org
hocomoco.autosome.org
February 17, 2025 at 11:38 AM
(1/4) Thrilled to announce another major release of the HOCOMOCO motif collection, well-known for its silly name and rigorous approach to constructing and benchmarking DNA sequence motifs recognized by human and mouse transcription factors.
hocomoco.autosome.org
hocomoco.autosome.org
(1/6) 🐦🔥 In IBIS #ibischallenge, we challenged teams from all over the world to decipher the DNA recognition code of human transcription factors. The IBIS Final Conference took place on November 27, 2024. Recordings and slides: disk.yandex.ru/d/82FEnwPn15...
November 28, 2024 at 7:59 PM
(1/6) 🐦🔥 In IBIS #ibischallenge, we challenged teams from all over the world to decipher the DNA recognition code of human transcription factors. The IBIS Final Conference took place on November 27, 2024. Recordings and slides: disk.yandex.ru/d/82FEnwPn15...
(10/12) Want something more than simple PWMs? Reaching the next level with IBIS: join the online IBIS conference on November 27, more details soon at ibis.autosome.org.
November 14, 2024 at 1:04 PM
(10/12) Want something more than simple PWMs? Reaching the next level with IBIS: join the online IBIS conference on November 27, more details soon at ibis.autosome.org.
(8/12) With that many PWMs at hand, we also demonstrate that several motifs can be easily combined into a better model with ArChIPelago: regression or Random Forest on top of PWM scans.
November 14, 2024 at 1:04 PM
(8/12) With that many PWMs at hand, we also demonstrate that several motifs can be easily combined into a better model with ArChIPelago: regression or Random Forest on top of PWM scans.
(7/12) Comparison of different tools yielded many surprises. On the one hand, underused motif discovery tools such as Dimont excel across different types of experimental data. On the other hand, no single tool or platform is enough to get the most for each TF and each data type.
November 14, 2024 at 1:04 PM
(7/12) Comparison of different tools yielded many surprises. On the one hand, underused motif discovery tools such as Dimont excel across different types of experimental data. On the other hand, no single tool or platform is enough to get the most for each TF and each data type.
(5/12) Testing all existing tools is hardly realistic. Focusing on position weight matrices (PWMs), we tested the most popular tools (e.g. MEME, HOMER), less popular yet powerful tools (e.g. ChIPMunk, Dimont), and a few advanced methods still able to yield PWMs (e.g. ExplaiNN, ProBound).
November 14, 2024 at 1:04 PM
(5/12) Testing all existing tools is hardly realistic. Focusing on position weight matrices (PWMs), we tested the most popular tools (e.g. MEME, HOMER), less popular yet powerful tools (e.g. ChIPMunk, Dimont), and a few advanced methods still able to yield PWMs (e.g. ExplaiNN, ProBound).
(4/12) We aimed to tackle both problems by running multiple motif discovery software against a large set of newly generated DNA on human TF-DNA interactions from Codebook [https://dx.doi.org/10.1101/2024.11.11.622097].
November 14, 2024 at 1:04 PM
(4/12) We aimed to tackle both problems by running multiple motif discovery software against a large set of newly generated DNA on human TF-DNA interactions from Codebook [https://dx.doi.org/10.1101/2024.11.11.622097].
(3/12) There are a multitude of high-throughput methods for assessing the DNA binding specificity of transcription factors. The variety of existing motif discovery tools is even greater. Which one to use?
November 14, 2024 at 1:04 PM
(3/12) There are a multitude of high-throughput methods for assessing the DNA binding specificity of transcription factors. The variety of existing motif discovery tools is even greater. Which one to use?
(2/12) The human genome encodes ~1.5 thousand transcription factors (TFs), and hundreds of them still lack "DNA motifs", i.e., compact human- and machine-readable representations of the TF-DNA binding specificity.
November 14, 2024 at 1:04 PM
(2/12) The human genome encodes ~1.5 thousand transcription factors (TFs), and hundreds of them still lack "DNA motifs", i.e., compact human- and machine-readable representations of the TF-DNA binding specificity.
(1/12) Excited to present the results of the large-scale benchmarking of DNA motif discovery tools using the Codebook data compendium on poorly studied human transcription factors and the Codebook Motif Explorer: dx.doi.org/10.1101/2024..., mex.autosome.org ⬇️.
November 14, 2024 at 1:04 PM
(1/12) Excited to present the results of the large-scale benchmarking of DNA motif discovery tools using the Codebook data compendium on poorly studied human transcription factors and the Codebook Motif Explorer: dx.doi.org/10.1101/2024..., mex.autosome.org ⬇️.