Biology subjects

Mount, S.

Publications and source records attributed to Mount, S..

5 recordsLinked to original sources

bioRxiv2024

Fair molecular feature selection unveils universally tumor lineage-informative methylation sites in colorectal cancer

In the era of precision medicine, performing comparative analysis over diverse patient populations is a fundamental step towards tailoring healthcare interventions. However, the critical aspect of equitably selecting molecular features across multiple patients is often overlooked. To address this challenge, we introduce FALAFL (FAir muLti-sAmple Feature seLection), an algorithmic approach based on combinatorial optimization. FALAFL is designed to bridge the gap between molecular feature selection and algorithmic fairness, ensuring a fair selection of molecular features from all patient samples in a cohort. We have applied FALAFL to the problem of selecting lineage-informative CpG sites within a cohort of colorectal cancer patients subjected to low read coverage single-cell methylation sequencing. Our results demonstrate that FALAFL can rapidly and robustly determine the optimal set of CpG sites, which are each well covered by cells across the vast majority of the patients, while ensuring that in each patient a high proportion of these sites have good read coverage. An analysis of the FALAFL-selected sites reveals that their tumor lineage-informativeness exhibits a strong correlation across a spectrum of diverse patient profiles. Furthermore, these universally lineage-informative sites are highly enriched in the inter CpG island regions. FALAFL integrates equity considerations into the molecular feature selection from single-cell sequencing data obtained from a patient cohort. We hope that it will help propel equitable healthcare data science practices and contribute to the advancement of our understanding of complex diseases.

bioinformatics↗

bioRxiv2023

Latent Dirichlet Allocation Mixture Models for Nucleotide Sequence Analysis

Strings of nucleotides that carry biological information are typically described using sequence motifs that can be represented by weight matrices or consensus sequences. However, many biological signals in DNA or RNA are recognized by multiple factors in temporal sequence, consist of a mixture of sometimes dissimilar alternative motifs, or may be described better by base composition. Here we apply the Latent Dirichlet Allocation (LDA) mixture model to nucleotide sequences, using k-mers as features, in three related approaches. First, positions in aligned sequences are used as samples. Alternatively, whole sequences are used as samples, either with positional k-mers or with bulk k-mers occurring throughout the sequence. LDA readily identifies motifs, including such elusive cases as the intron branch site. LDA can also identify subtypes of sequence, such as splice site subtypes enriched in long vs. short introns, and can reliably distinguish such properties as reading frame or species of origin. Our results show that LDA is a useful model for describing heterogeneous signals, for assigning individual sequences to subtypes, and for identifying and characterizing sequences that do not fit recognized subtypes. Because LDA topic models are interpretable, they also aid the discovery of new motifs, even those present in a small fraction of samples, allowing a user to hypothesize potential regulatory factors. In summary, LDA can identify and characterize signals in nucleotide sequences and is useful for identifying candidate regulatory factors involved in complex biological processes.

bioinformatics↗

bioRxiv2021

Epigenomic tumor evolution modeling with single-cell methylation data profiling

Recent studies exploring the impact of methylation in tumor evolution suggest that while the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Since changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumors single-cell methylation lineage tree and jointly identifying lineage-informative CpG sites which harbor changes in methylation status that are retained along the lineage. We apply Sgootr on the single-cell bisulfite-treated whole genome sequencing data of multiregionally-sampled tumor cells from 9 metastatic colorectal cancer patients made available by Bian et al., as well as multiregionally-sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient made available by Chaligne et al.. We demonstrate that the tumor lineages constructed reveal a simple model underlying colorectal tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and more in concordance with the sequential-progression model of tumor evolution, in time a fraction of that used in prior studies. Interestingly, lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to CGIs, which have been the main regions of interest in genomic methylation-related analyses. Sgootr is implemented as a Snakemake workflow, available at https://github.com/algo-cancer/Sgootr. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/436475v2_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1d3920aorg.highwire.dtl.DTLVardef@11905d4org.highwire.dtl.DTLVardef@1cda90aorg.highwire.dtl.DTLVardef@114ea18_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2019

Draft Genome Assembly and Annotation of Red Raspberry Rubus Idaeus

The red raspberry, Rubus idaeus, is widely distributed in all temperate regions of Europe, Asia, and North America and is a major commercial fruit valued for its taste, high antioxidant and vitamin content. However, Rubus breeding is a long and slow process hampered by limited genomic and molecular resources. Genomic resources such as a complete genome sequencing and transcriptome will be of exceptional value to improve research and breeding of this high value crop. Using a hybrid sequence assembly approach including data from both long and short sequence reads, we present the first assembly of the Rubus idaeus genome (Joan J. variety). The de novo assembled genome consists of 2,145 scaffolds with a genome completeness of 95.3% and an N50 score of 638 KB. Leveraging a linkage map, we anchored 80.1% of the genome onto seven chromosomes. Using over 1 billion paired-end RNAseq reads, we annotated 35,566 protein coding genes with a transcriptome completeness score of 97.2%. The Rubus idaeus genome provides an important new resource for researchers and breeders.

genomics↗

bioRxiv2018

Fast and interpretable alternative splicing and differential gene-level expression analysis using transcriptome segmentation with Yanagi

IntroductionAnalysis of differential alternative splicing from RNA-seq data is complicated by the fact that many RNA-seq reads map to multiple transcripts, besides, the annotated transcripts are often a small subset of the possible transcripts of a gene. Here we describe Yanagi, a tool for segmenting transcriptome to create a library of maximal L-disjoint segments from a complete transcriptome annotation. That segment library preserves all transcriptome substrings of length L and transcripts structural relationships while eliminating unnecessary sequence duplications.\n\nContributionsIn this paper, we formalize the concept of transcriptome segmentation and propose an efficient algorithm for generating segment libraries based on a length parameter dependent on specific RNA-Seq library construction. The resulting segment sequences can be used with pseudo-alignment tools to quantify expression at the segment level. We characterize the segment libraries for the reference transcriptomes of Drosophila melanogaster and Homo sapiens and provide gene-level visualization of the segments for better interpretability. Then we demonstrate the use of segments-level quantification into gene expression and alternative splicing analysis. The notion of transcript segmentation as introduced here and implemented in Yanagi opens the door for the application of lightweight, ultra-fast pseudo-alignment algorithms in a wide variety of RNA-seq analyses.\n\nConclusionUsing segment library rather than the standard transcriptome succeeds in significantly reducing ambigious alignments where reads are multimapped to several sequences in the reference. That allowed avoiding the quantification step required by standard kmer-based pipelines for gene expression analysis. Moreover, using segment counts as statistics for alternative splicing analysis enables achieving comparable performance to counting-based approaches (e.g. rMATS) while rather using fast and lighthweight pseudo alignment.

bioinformatics↗