Sina Majidian
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sinamajidian.bsky.social
Sina Majidian
@sinamajidian.bsky.social
1.3K followers 1.6K following 130 posts
On the academic job market | How are species compared to one another across different genomic regions? Postdoc at Langmead Lab, Johns Hopkins | Comparative #genomics at scale | Formerly at UNIL/SIB/WUR | sinamajidian.github.io
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sinamajidian.bsky.social
Specificity, length and luck drive gene rankings in association studies
nature.com/articles/s41586-025-09703-7
a,b, Schematics of GWAS (a) and LoF burden tests (b). c, Each cell is a genome-wide significant gene according to LoF burden tests, ordered by significance (most significant (top) to least significant (bottom)). Genes are coloured by the rank of the locus that contains the gene according to GWAS or are coloured black if they are not contained in any genome-wide significant locus. Across 151 traits with at least one burden hit and one GWAS hit, 74.6% (1,382 out of 1,852) of genome-wide significant burden test hits fall within a GWAS locus. Trait abbreviations are defined in Supplementary Table 1. d, Minimum LoF burden test P values for any gene overlapping a genome-wide significant GWAS locus plotted against the minimum GWAS P value within that locus (n = 382 loci). e, The genomic region surrounding NPR2. GWAS P values of approximately independent genome-wide significant GWAS hits (top), and the location of genes coloured by LoF burden test P values (bottom) are shown. f, Similar to panel e but for the genomic region surrounding HHIP. a, A cartoon of two genes that affect a trait under study. The widths of the arrows represent the relative effect sizes. Gene 1 is more trait specific, but gene 2 is more trait important. b, Formal definitions of trait importance and trait specificity for genes in the context of LoF burden tests.
November 9, 2025 at 12:52 PM
sinamajidian.bsky.social
Next, Johanna von Wachsmann
@johannavw.bsky.social presented Gemsparcl—Rapid and consistent genome clustering for navigating bacterial diversity with millions of genomes" #GI2025
November 8, 2025 at 4:23 PM
sinamajidian.bsky.social
Jim Shaw @jimshaw.bsky.social presented "High-resolution metagenome assembly for modern long reads
with myloasm"
This efficient tool uses SNPmers and leverages high quality ONT and PacBio reads for metagenome assembly.
myloasm: doi.org/10.1101/2025.09.05.674543
Algorithm overview of myloasm. A. SNPmers—k-mers that have polymorphic middle bases— are called from all sequences. Long reads are then indexed with SNPmers and open syncmers. B. Inexact repeats between or within genomes create multiple paths in an assembly graph. Myloasm allows for errors but penalizes for mismatched SNPmers, creating a simpler assembly graph without error correction. C. Reads are mapped to and coverage is calculated. Myloasm calculates several coverages at different sequence identity thresholds. These coverages are used to define a probabilistic model on paths, where paths with consistent coverage have higher probability. Edges are weighted by the path probabilities, then an iterative cleaning approach is used to clean up the graph by cutting lowly weighted edges while respecting the graph topology. MAG and contig recovery results for real metagenomes. A. MAG recovery after assembly and binning. Dark bars represent circular contigs with > 90% completeness and < 5% contamination. Medium bars represent non-circular MAGs with > 90% completeness and < 5% contamination. Light bars represent MAGs with > 90% completeness and < 5% contamination. NanoMDBG is used for ONT reads whereas metaMDBG is used for HiFi reads. B. Top: number of high-quality contigs with > 90% completeness and < 5% contamination on two different gut samples from Minich et al. sequenced with HiFi and ONT R10.4 data. Bottom: the same datasets but counting only circular high-quality contigs. Backgrounds are colored by gut sample.
November 8, 2025 at 4:21 PM
sinamajidian.bsky.social
Anshul Kundaje @anshulkundaje.bsky.social presented "Deep learning models of regulatory DNA—A comparison of model
design choices" #GI2025 He focused more on task-specific models & showed multi-task models lack causal interpretability.
ChromBPNet: doi.org/10.1101/2024.12.25.630221
encodeproject.org
November 8, 2025 at 4:16 PM
sinamajidian.bsky.social
Today is the last day of Genome Informatics #GI2025. The first talk was presented by Genrietta Yagudayeva on "A reproducible RNA-seq pipeline for mitogenomics and barcoding phylogenetics in neglected biodiversity"
November 8, 2025 at 4:09 PM
sinamajidian.bsky.social
Yana Safonova (Penn State) delivered the keynote "Enabling biomedical discoveries through immunogenomics
approaches"

Mammalian Immune Luci: doi.org/10.1093/molbev/msaf152
PatchWorkPlot: doi.org/10.1093/bioinformatics/btaf504
Overview of IG/TR loci collected across 46 mammalian species. a) The phylogenetic tree of mammalian species selected for the analysis. The tree was constructed using consensus topologies derived from 100 trees obtained from VertLife.org (Upham et al. 2019). The final consensus tree was generated with TreeAnnotator v1.10.4 (Drummond and Rambaut 2007) using mean node heights. Taxonomic orders are shown next to the species names. Counts of productive V genes in IGH, IGK, IGL, TRA, and TRB loci are shown on the heatmap on the right. Distal IG/TR loci are shown as red rectangles. A locus is marked with ◖/◖◗ if it has one/two intact haplotype(s) assembled. Counts of species with one/two intact haplotype(s) of IG/TR loci are summarized in the heatmap below. b) Number of proximal parts versus the total locus length across ten distal IG/TR loci. c) Lengths of proximal and distal IG/TR loci. Here and further error bars represent 95% confidence intervals; P-values are denoted as follows: ns: P ≥ 0.05; *<0.05, **<0.01, ***<0.001, ****<0.0001; and P-values are computed using the Kruskal–Wallis test unless specified otherwise. d) Counts of productive V genes in proximal and distal IG/TR loci.
November 7, 2025 at 10:34 PM
sinamajidian.bsky.social
Ryan Moreno from @sroyyors.bsky.social Lab presented "Integrating single-cell omics data across species using matrix factorization regularized by gene-level phylogenies"

A very cool application of gene orthology across species for single-cell expression analysis! #orthology #GI2025 #singlecell
November 7, 2025 at 10:23 PM
sinamajidian.bsky.social
Li Song @mourisl.bsky.social presented "Quality control of single-cell ATAC-seq data without peak calling using Chromap"

Chromap-QC: biorxiv.org/content/10.1101/2025.07.15.664951
Chromap: nature.com/articles/s41467-021-26865-w #GI2025
November 7, 2025 at 7:18 PM
sinamajidian.bsky.social
Hanchen Wang presented "Biomni—A general-purpose biomedical AI agent" #GI2025
doi.org/10.1101/2025.05.30.656746
November 7, 2025 at 6:57 PM
sinamajidian.bsky.social
Matthew Nguyen delivered a great talk on "Refining Kraken2 long-read taxonomic classifications using convolutional neural networks"
November 7, 2025 at 6:41 PM
sinamajidian.bsky.social
Yijie Kang (CSHL, Stony Brook) from @pkoo562.bsky.social Lab presented "Decoding the sequence basis of Pol II elongation with deep learning"
November 7, 2025 at 3:05 PM
sinamajidian.bsky.social
Third day of Genome Informatics #GI2025 began with an exciting session on “AI, ML and Integrative Genomics” chaired by Irene Kaplow & Thomas Pierrot.
The first talk, by Irene Kaplow, focused on Challenges in Predicting Enhancer Activity Differences Between Species
doi.org/10.1186/s12864-022-08450-7
OCR Ortholog Open Chromatin Status Prediction Framework Overview. a We trained a convolutional neural network (CNN) for predicting brain open chromatin using sequences underlying brain open chromatin region (OCR) orthologs in a small number of species and used the CNN to predict brain OCR ortholog open chromatin status across the species in the Zoonomia Consortium. Specifically, we used the sequences underlying the orthologs for which we have brain open chromatin data to train a CNN for predicting open chromatin. Then, we used the CNN to predict the probability of brain open chromatin for all brain OCR orthologs; predictions are illustrated on the right. Animals for which we do not have open chromatin data are in dark gray instead of black to indicate that their brain open chromatin is imputed. While we cannot evaluate the accuracy of most of our predictions, obtaining open chromatin data from most tissues in most species is infeasible, so predictions might be the best OCR annotations that we can obtain. b To demonstrate that our models can accurately predict whether sequence differences between species are associated with open chromatin differences, in addition to the evaluations described in previous work [57], we evaluated our performance on species-specific open chromatin for a species not used in model training and clade-specific open and closed chromatin for clades not used in model training. Since such regions often comprise a minority of OCR orthologs, models could obtain good overall performance while obtaining poor performance on such regions. We also evaluated our performance on tissue-specific open and closed chromatin for a tissue not used in model training, where we expect models to predict 0 if model learns sequence signatures related to the tissue used in training. c Full mouse test set and lineage-specific OCR accuracy evaluations for mouse sequence-only brain model, illustrating that, even for the best of these models,
November 7, 2025 at 2:28 PM
sinamajidian.bsky.social
Second's day concluded by fantastic talk by Cristina Martin Linares on "Minimal reconstruction of SpliceAI using distilled matryoshka sparse autoencoders"

They showed that matryoshka SAEs arxiv.org/abs/2503.17547 improves upon openSpliceAI elifesciences.org/reviewed-preprints/107454. #GI2025
https://arxiv.org/abs/2503.17547 Learning Multi-Level Features with Matryoshka Sparse Autoencoders
November 7, 2025 at 1:08 PM
sinamajidian.bsky.social
Nicola De Maio presented "Maximum likelihood phylogenetics at pandemic scales" and discussed the importance of scalable phylogenetics in genomic epidemiology. #GenomeInformatics #GI2025
MAPLE: nature.com/articles/s41588-023-01368-0
a, Left—Fasta representation of an individual SARS-CoV-2 genome consists of sample name followed by the entire ≈ 30 kbp genome sequence. Right—MAPLE format records only the differences between the genome under consideration and a reference; columns represent the variant character observed, the position along the genome and (when necessary) the number of consecutive positions for which the character is observed. b, Left—an example likelihood vector at an internal node of a phylogenetic tree (shown by the narrow blue arrow; only a small portion of the tree is shown); for simplicity, we show only ten genome positions. At each position (rows 1–10), each column contains the likelihood for a specific nucleotide. For rows 1–9, the likelihood is concentrated at only one nucleotide (highlighted in green), while for position 10, we show an example with more uncertainty. Right—MAPLE representation of these node likelihoods. Assuming that the reference sequence at the first nine positions matches the most likely nucleotides in the vector (ATTAAAGGT), then for positions 1–9, the likelihood of nonreference nucleotides is negligible and we represent the likelihoods with a single symbol (R). At position 10, due to non-negligible uncertainty, we explicitly calculate and store the four relative likelihoods. c, Examples of likelihood calculation steps in MAPLE. Red arrows represent the flow of information from the tips to the root of the tree. Left—if two child nodes are in reference state R for a region of the genome (here, positions 1–9), then MAPLE assumes that their parent is also in state R. Right—if at a genome position (here, position 10), two child nodes have likelihoods concentrated at different nucleotides, then for their parent, we explicitly calculate the relative likelihoods of all four nucleotides.
November 7, 2025 at 12:56 PM
sinamajidian.bsky.social
Nicolae Sapoval @nsapoval.bsky.social presented "Theoretical and empirical performance of pseudo-likelihood- based Bayesian inference of species trees under the multispecies coalescent"
A fantastic theory talk, offering intuitive insights!
Paper: doi.org/10.1101/2025.01.28.635282
November 6, 2025 at 8:28 PM
sinamajidian.bsky.social
"PhyloFisher v2—Advancing accuracy and reproducibility in deep phylogenomics" was presented by Robert E. Jones.
doi.org/10.1371/journal.pbio.3001365 & doi.org/10.1002/cpz1.969
Software: github.com/TheBrownLab/PhyloFisher
November 6, 2025 at 8:18 PM
sinamajidian.bsky.social
The "Algorithmic and evolutionary biology" session is chaired by Nicola De Maio and Erin Molloy.
Erin Molloy delivered the first talk titled "Towards scalable reconstruction of species phylogenies under the network multispecies coalescent"
TreeQMC: doi.org/10.1093/sysbio/syaf009
November 6, 2025 at 8:15 PM
sinamajidian.bsky.social
The keynote speaker, Marinka Zitnik @marinkazitnik.bsky.social , delivered her talk to a full house on "Empowering biomedical discovery with AI scientists"
Paper: arxiv.org/abs/2509.23426
Platform: aiscientist.tools
November 6, 2025 at 7:58 PM
sinamajidian.bsky.social
The morning session concluded with a talk by Megan Le: DeKnot—Local haplotype-resolved assembly with k-syncmer-
based multiplex De Bruijn graphs
The goal is to perform local haplotype assembly by iteratively growing lists of k’ consecutive closed syncmers.
November 6, 2025 at 6:46 PM
sinamajidian.bsky.social
Haonan Wu gives a talk on "A k-mer-based estimator of the substitution rate between repetitive sequences"
www.biorxiv.org/content/10.1...
This work tackles the issue of Mash which ignores repeats in the genome, providing better distance estimation #GI2025
November 6, 2025 at 4:38 PM
sinamajidian.bsky.social
Mile Sikic @msikic.bsky.social presents "AI for genomes—Rethinking de novo assembly" genome.cshlp.org/content/35/4/839
They devised a bidirectional message-passing procedure in GNN for the problem of genome assembly
November 6, 2025 at 4:22 PM
sinamajidian.bsky.social
Harun Mustafa presents "Efficient, accurate, SRA-scale indexing and query" at #GI2025
MetaGraph is a highly compressed representation
of all public biological sequences!
nature.com/articles/s41586-025-09603-w
Try it online: metagraph.ethz.ch/search
November 6, 2025 at 4:09 PM
sinamajidian.bsky.social
Ke Chen presents "A scalable and improved heuristic for flow decomposition"
They developed a new algorithm to decompose a directed graph (generated from reads) into a minimum # weighted paths, with application to metagenome and transcriptome assembly.
November 6, 2025 at 2:46 PM
sinamajidian.bsky.social
Second day of Genome Informatics #GI2025 began with the session “Genome Assembly and Sequence Algorithms" Yun William Yu presented “Average-case Analysis of Seed-Chain-Extend under Random Mutations"
genome.cshlp.org/content/33/7/1175
providing theoretical guarantees for the popular seed-chain-extend
Abstract: Seed-chain-extend with k-mer seeds is a powerful heuristic technique for sequence alignment used by modern sequence aligners. Although effective in practice for both runtime and accuracy, theoretical guarantees on the resulting alignment do not exist for seed-chain-extend. In this work, we give the first rigorous bounds for the efficacy of seed-chain-extend with k-mers in expectation. Assume we are given a random nucleotide sequence of length ∼n that is indexed (or seeded) and a mutated substring of length ∼m ≤ n with mutation rate θ < 0.206. We prove that we can find a k = Θ(log n) for the k-mer size such that the expected runtime of seed-chain-extend under optimal linear-gap cost chaining and quadratic time gap extension is O(mn^f(θ) log n), where f(θ) < 2.43 · θ holds as a loose bound. The alignment also turns out to be good; we prove that more than 1-o(sqrt(1/m)) fraction of the homologous bases is recoverable under an optimal chain. We also show that our bounds work when k-mers are sketched, that is, only a subset of all k-mers is selected, and that sketching reduces chaining time without increasing alignment time or decreasing accuracy too much, justifying the effectiveness of sketching as a practical speedup in sequence alignment. We verify our results in simulation and on real noisy long-read data and show that our theoretical runtimes can predict real runtimes accurately. We conjecture that our bounds can be improved further, and in particular, f(θ) can be further reduced.
November 6, 2025 at 2:19 PM
sinamajidian.bsky.social
Nicole Brown gave a fantastic talk on Identifying introgressions across pangenomes with Panagram

It uses k-mer conservation to annotate genomic variation across hundreds of genomes, followed by normalization of k-mer profiles to identify introgression events
github.com/kjenike/pana... #GI2025
November 6, 2025 at 2:52 AM