Biology subjects

Lima, L.

Publications and source records attributed to Lima, L..

At least 19 recordsLinked to original sources

bioRxiv2026

gene2fish: a multi-gene spatial expression browser for in situ hybridization data across zebrafish development

Single-cell RNA-sequencing (scRNA-seq) atlases of zebrafish development have catalogued marker genes for hundreds of cell types, but transcriptomic data alone cannot reveal their expression patterns in the embryo. The systematically annotated Thisse high-throughput whole-mount in situ hybridization (ISH) collection at ZFIN provides spatial expression data across twelve developmental stages, yet the ZFIN web interface is organized around individual genes, making side-by-side cross-gene comparison laborious. We present gene2fish, a web application that displays ISH images from the Thisse dataset, linked directly to their ZFIN records, in a gene-by-developmental-stage grid. With gene2fish, researchers can quickly explore and validate the spatial expression of marker gene lists, and find the genes annotated to specific anatomical structures across developmental time. The application is part of Zebrahub and is freely available at https://zebrahub.sf.czbiohub.org/gene2fish.

developmental biology↗

bioRxiv2026

RNAquarium: an archive-scale atlas of zebrafish gene expression coupled with pan-taxonomic profiling reveals diverse viral drivers of transcriptomic states

Zebrafish RNA-seq studies span diverse developmental, physiological, and disease contexts, yet most analyses remain confined to individual experiments and disregard the non-zebrafish component of the data. We present RNAquarium, a scalable framework for joint transcriptomic and metatranscriptomic analysis of RNA-seq data and apply it to all publicly available zebrafish RNA-seq datasets in the Sequence Read Archive. This resource captures transcriptomic structure across development and tissues, reveals diverse microbial and viral associations, and identifies previously undescribed zebrafish viruses including a close relative of human influenza B virus linked to distinct host transcriptional states. We further demonstrate that archive-scale transcriptomes can support foundation-model training and prediction of infection-associated transcriptomic signatures. RNAquarium provides an open framework and interactive portal for exploring the breadth of zebrafish gene expression patterns and associated taxa profiled across a large re-search community and establishes a generalizable strategy for integrating transcriptomic and metatranscriptomic analyses across the diversity of life represented in public sequencing archives.

genomics↗

bioRxiv2025

Amira: gene-space de Bruijn graphs to improve the detection of AMR genes from bacterial long reads

Accurate detection of antimicrobial resistance (AMR) genes is essential for the surveillance, epidemiology and genotypic prediction of AMR. This is typically done by generating an assembly from the sequencing reads of a bacterial isolate and running AMR gene detection tools on the assembly. However, despite advances in long-read sequencing that have greatly improved the quality and completeness of bacterial genome assemblies, assembly tools remain prone to large-scale errors caused by repeats in the genome, leading to inaccurate detection of AMR gene content and consequent impact on resistance prediction. In this work we present Amira, a tool to detect AMR genes directly from unassembled long-read sequencing data. Amira leverages the fact that multiple consecutive genes lie within a single read to construct gene-space de Bruijn graphs where the k-mer alphabet is the set of genes in the pan-genome of the species under study. Through this approach, the reads corresponding to different copies of AMR genes can be effectively separated based on the genomic context of the AMR genes, and used to infer the nucleotide sequence of each copy. Amira achieves significant improvements in genomic copy number recall and nucleotide accuracy, demonstrated through objective simulations and comparison with alternative read and assembly-based methods on samples with manually curated truth assemblies. Applied to a dataset of 32 Escherichia coli samples with diverse AMR gene content, Amira achieves a mean genomic-copy-number recall of 98.4% with precision 97.9% and nucleotide accuracy 99.9%. Finally, we show that Amira consistently detects more true AMR genes across all E. coli, K. pneumoniae and E. faecium nanopore datasets from the ENA (n=8580, 2448 and 415 respectively) than an assembly-based approach.

bioinformatics↗

bioRxiv2024

Pre and Post antibiotic epoch: insights into the historical spread of antimicrobial resistance

Plasmids are now the primary vectors of antimicrobial resistance, but our understanding of how human industrialisation of antibiotics influenced this is limited by a paucity of data predating the antibiotic era (PAE). By investigating plasmids from clinically relevant bacteria isolated between 1917 and 1954 and comparing them to modern plasmids, we captured over 100 years of evolution. We show that while all PAE plasmids were devoid of resistance genes and most never acquired them, a small minority evolved to drive the global spread of resistance to first-line and last-resort antibiotics in Gram-negative bacteria. They have evolved through complex microevolution and fusion events into a distinct group of highly recombinogenic, multi-replicon, self-transmissible plasmids that now pose the highest risk to resistance dissemination, and therefore human health. One Sentence SummaryPre-antibiotic era bacteria reveal the origin and evolution of drug-resistance vectors.

genomics↗

bioRxiv2024

Applying rearrangement distances to enable plasmid epidemiology with pling

Plasmids are a key vector of antibiotic resistance, but the current bioinformatics toolkit is not well suited to tracking them. The rapid structural changes seen in plasmid genomes present considerable challenges to evolutionary and epidemiological analysis. Typical approaches are either low resolution (replicon typing) or use shared k-mer content to define a genetic distance. However this distance can both overestimate plasmid relatedness by ignoring rearrangements, and underestimate by over-penalising gene gain/loss. Therefore a model is needed which captures the key components of how plasmid genomes evolve structurally - through gene/block gain or loss, and rearrangement. A secondary requirement is to prevent promiscuous transposable elements (TEs) leading to over-clustering of unrelated plasmids. We choose the "Double Cut and Join Indel" model, in which plasmids are studied at a coarse level, as a sequence of signed integers (representing genes or aligned blocks), and the distance between two plasmids is the minimum number of rearrangement events or indels needed to transform one into the other. We show how this gives much more meaningful distances between plasmids. We introduce a software workflow pling (https://github.com/iqbal-lab-org/pling), which uses the DCJ-Indel model, to calculate distances between plasmids and then cluster them. In our approach, we combine containment distances and DCJ-Indel distances to build a TE-aware plasmid network. We demonstrate superior performance and interpretability to other plasmid clustering tools on the "Russian Doll" dataset and a hospital transmission dataset. Impact statementStudying plasmid transmission is a necessary component of understanding antibiotic resistance spread, but identifying recently related plasmids is difficult and often requires manual curation. Pling simplifies this by leveraging a combination of containment distances and rearrangement distances to cluster plasmids. The outcome are clusters of recently related plasmids with a clear backbone and relatively large core genomes, in contrast to other tools which sometimes overcluster. Additionally the network constructed by pling provides a framework with which to spot evolutionary events, such as potential fusions of plasmids and spread of transposable elements. Data summarySupplementary information and figures are available as an additional PDF. The tool presented in this paper is available under https://github.com/iqbal-lab-org/pling. Additional computational analysis and scripts are described and provided under https://github.com/babayagaofficial/pling_paper_analyses. The sequence data used can be found under BioProject no. PRJNA246471 in the National Center for Biotechnology Information for the "Russian doll" dataset (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA246471), and under Project no. PRJEB31034 in European Nucleotide Archive for the "Addenbrookes" dataset (https://www.ebi.ac.uk/ena/browser/view/PRJEB30134). All other genome sequences used were sourced from PLSDB (https://ccb-microbe.cs.uni-saarland.de/plsdb/), and lists of accession numbers can be found in the additional analysis github.

bioinformatics↗

bioRxiv2024

AllTheBacteria - all bacterial genomes assembled, available and searchable

Public microbial genomes encode an immense record of biological diversity, evolution and molecular function, but much of this information remains difficult to reuse because raw sequencing data are not uniformly assembled, quality controlled, annotated or searchable at scale. Here we present AllTheBacteria, an open, community-built resource that transforms public bacterial short-read whole-genome sequencing reads into a uniformly processed discovery platform. The current analysed release contains 2,440,377 high-quality bacterial and archaeal genomes from 11,273 species, together with standardized taxonomic assignments, genome annotations, antimicrobial resistance calls, antiphage-defence annotations, protein structure predictions and AI-ready sequence tables. We show that this infrastructure enables applications that would otherwise be impractical, from global sequence search and outbreak contextualization to pangenome method development, antimicrobial resistance reservoir mapping and antiphage-defence ecology. As a stringent experimental demonstration, we mined 3,919,096 encrypted peptide fragments from AllTheBacteria proteomes using our deep learning model APEX 1.1, identifying 1,867 candidates with predicted antimicrobial activity. We synthesized 24 representative peptides and tested them against 20 clinically relevant bacterial strains, including antibiotic-resistant pathogens. Multiple peptides showed low-micromolar activity, membrane-responsive conformational transitions and selective envelope perturbation. A lead molecule, ATB20, reduced Acinetobacter baumannii burden in a murine skin abscess model with efficacy comparable to polymyxin B and no overt toxicity. Together, these results establish AllTheBacteria as both a foundational community resource for microbiology and a renewable engine for AI-guided antimicrobial discovery.

bioinformatics↗

bioRxiv2023

Multilevel Plasticity and Altered Glycosylation Drive Aggressiveness in Hypoxic and Glucose-Deprived Bladder Cancer Cells

Bladder tumours with aggressive characteristics often present with microenvironmental niches marked by low oxygen levels (hypoxia) and limited glucose supply due to inadequate vascularization. The molecular mechanisms facilitating cellular adaptation to these stimuli remain largely elusive. Employing a multi-omics approach, we discovered that hypoxic and glucose- deprived cancer cells enter a quiescent state supported by mitophagy, fatty acid {beta}-oxidation, and amino acid catabolism, concurrently enhancing their invasive capabilities. Reoxygenation and glucose restoration efficiently reversed cell quiescence without affecting cellular viability, highlighting significant molecular plasticity in adapting to microenvironmental challenges. Furthermore, cancer cells exhibited substantial perturbation of protein O-glycosylation, leading to simplified glycophenotypes with shorter glycosidic chains. Exploiting glycoengineered cell models, we established that immature glycosylation contributes to reduced cell proliferation and increased invasion. Our findings collectively indicate that hypoxia and glucose deprivation trigger cancer aggressiveness, reflecting an adaptive escape mechanism underpinned by altered metabolism and protein glycosylation, providing grounds for clinical intervention.

biochemistry↗

bioRxiv2023

Gene editing in the Chagas disease vector Rhodnius prolixus by Cas9-mediated ReMOT Control

Rhodnius prolixus is currently the model vector of choice for studying Chagas disease transmission, a debilitating disease caused by Trypanosoma cruzi parasites. Despite the broad use of double-stranded RNA interference for the knockdown of gene function in R. prolixus, transgenesis and gene editing protocols are still lacking. Here we tested Receptor-Mediated Ovary Transduction of Cargo (ReMOT Control) and direct parental injection of CRISPR (DIPA-CRISPR) for the maternal delivery of CRISPR/Cas9 elements to developing R. prolixus oocytes, and strategies for the identification of insertions/deletions (indels) in target loci of resulting gene-edited G0 nymphs. We demonstrate successful ReMOT Control-mediated gene editing of the eye color markers Rp-scarlet and Rp-white, and the cuticle color marker Rp-yellow, with highest effectiveness obtained using the ovary-targeting BtKV ligand. These results provide proof-of-concepts for generating somatic mutations in R. prolixus and potentially for generating germline edited lines in triatomines. Our studies also suggest that optimal strategies for recovery of mutations include performing multiple gRNA injections and the use of visible phenotypes such as those displayed in the Rp-scarlet, Rp-white and Rp-yellow loci for future Co-CRISPR experiments. These results will lay the foundation for gene editing protocols for triatomines and could lead to the development of novel control strategies for vectors of Chagas disease. Author SummaryRhodnius prolixus is an insect vector of the protozoan Trypanossoma cruzi, causative agent of debilitating Chagas disease. To fight the spread of the disease, it has been suggested that biological control of the insect should be attempted. Gene editing by the novel CRISPR methodology holds great promise in this sense, as it enables to target almost any gene in the genome for mutagenesis, thus allowing the control of insect physiology and reproduction. Here we have tested protocols for the delivery of CRISPR reagents as an attempt to enable genome editing of the vector. Our results show that maternal delivery of CRISPR by the ReMOT Control method is efficient for mutating eye and cuticle color genes in the resulting nymphs, generating edited animals with red eyes, white eyes or a yellow cuticle. This is the first report of gene editing in a vector of Chagas disease and should lay the basis to produce modified animals either unable to carry the T. cruzi parasite or to reproduce.

molecular biology↗

bioRxiv2023

Drug resistance prediction for Mycobacterium tuberculosis with reference graphs

2.The dominant paradigm for analysing genetic variation relies on a central idea: all genomes in a species can be described as minor differences from a single reference genome. However, this approach can be problematic or inadequate for bacteria, where there can be significant sequence divergence within a species. Reference graphs are an emerging solution to the reference bias issues implicit in the "single-reference" model. Such a graph represents variation at multiple scales within a population - e.g., nucleotide- and locus-level. The genetic causes of drug resistance in bacteria have proven comparatively easy to decode compared with studies of human diseases. For example, it is possible to predict resistance to numerous anti-tuberculosis drugs by simply testing for the presence of a list of single nucleotide polymorphisms and insertion/deletions, commonly referred to as a catalogue. We developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a Mycobacterium tuberculosis drug resistance reference graph, a process that can be replicated for other species. The graph is built from a global dataset of isolates with varying drug susceptibility profiles, thus capturing common and rare resistance- and susceptible-associated haplotypes. We benchmark DrPRG against the existing graph-based tool Mykrobe and the haplotype-based approach of TBProfiler using 44,709 and 138 publicly available Illumina and Nanopore samples with associated phenotypes. We find DrPRG has significantly improved sensitivity and specificity for some drugs compared to these tools, with no significant decreases. It uses significantly less computational memory than both tools, and provides significantly faster runtimes, except when runtime is compared to Mykrobe on Nanopore data. We discover and discuss novel insights into resistance-conferring variation for M. tuberculosis - including deletion of genes katG and pncA - and suggest mutations that may warrant reclassification as associated with resistance. 3. Impact statementMycobacterium tuberculosis is the bacterium responsible for tuberculosis (TB). TB is one of the leading causes of death worldwide; before the coronavirus pandemic it was the leading cause of death from a single pathogen. Drug-resistant TB incidence has recently increased, making the detection of resistance even more vital. In this study, we develop a new software tool to predict drug resistance from whole-genome sequence data of the pathogen using new reference graph models to represent a reference genome. We evaluate it on M. tuberculosis against existing tools for resistance prediction and show improved performance. Using our method, we discover new resistance-associated variations and discuss reclassification of a selection of existing mutations. As such, this work contributes to TB drug resistance diagnostic efforts. In addition, the method could be applied to any bacterial species, so is of interest to anyone working on antimicrobial resistance. 4. Data summaryThe authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. The software method presented in this work, DrPRG, is freely available from GitHub under an MIT license at https://github.com/mbhall88/drprg. We used commit 9492f25 for all results via a Singularity[1] container from the URI docker://quay.io/mbhall88/drprg:9492f25. All code used to generate results for this study are available on GitHub at https://github.com/mbhall88/drprg-paper. All data used in this work are freely available from the SRA/ENA/DRA and a copy of the datasheet with all associated phenotype information can be downloaded from the archived repository at https://doi.org/10.5281/zenodo.7819984 or found in the previously mentioned GitHub repository. The Mycobacterium tuberculosis index used in this work is available to download through DrPRG via the command drprg index --download mtb@20230308 or from GitHub at https://github.com/mbhall88/drprg-index.

bioinformatics↗

bioRxiv2023

Efficient and Robust Search of Microbial Genomes via Phylogenetic Compression

Comprehensive collections approaching millions of sequenced genomes have become central information sources in the life sciences. However, the rapid growth of these collections has made it effectively impossible to search these data using tools such as BLAST and its successors. Here, we present a technique called phylogenetic compression, which uses evolutionary history to guide compression and efficiently search large collections of microbial genomes using existing algorithms and data structures. We show that, when applied to modern diverse collections approaching millions of genomes, lossless phylogenetic compression improves the compression ratios of assemblies, de Bruijn graphs, and k-mer indexes by one to two orders of magnitude. Additionally, we develop a pipeline for a BLAST-like search over these phylogeny-compressed reference data, and demonstrate it can align genes, plasmids, or entire sequencing experiments against all sequenced bacteria until 2019 on ordinary desktop computers within a few hours. Phylogenetic compression has broad applications in computational biology and may provide a fundamental design principle for future genomics infrastructure.

bioinformatics↗

bioRxiv2022

A Comprehensive Enumeration of the Human Proteostasis Network. 1. Components of Translation, Protein Folding, and Organelle-Specific Systems

The condition of having a healthy, functional proteome is known as protein homeostasis, or proteostasis. Establishing and maintaining proteostasis is the province of the proteostasis network, approximately 2,500 genes that regulate protein synthesis, folding, localization, and degradation. The proteostasis network is a fundamental entity in biology with direct relevance to many diseases of protein conformation. However, it is not well defined or annotated, which hinders its functional characterization in health and disease. In this series of manuscripts, we aim to operationally define the human proteostasis network by providing a comprehensive, annotated list of its components. Here, we provide a curated list of 959 unique genes that comprise the protein synthesis machinery, chaperones, folding enzymes, systems for trafficking proteins into and out of organelles, and organelle-specific degradation systems. In subsequent manuscripts, we will delineate the human autophagy-lysosome pathway, the ubiquitin-proteasome system, and the proteostasis networks of model organisms.

bioinformatics↗

bioRxiv2021

CALDERA: Finding all significant de Bruijn subgraphs for bacterial GWAS

Genome wide association studies (GWAS), aiming to find genetic variants associated with a trait, have widely been used on bacteria to identify genetic determinants of drug resistance or hypervirulence. Recent bacterial GWAS methods usually rely on k-mers, whose presence in a genome can denote variants ranging from single nucleotide polymorphisms to mobile genetic elements. Since many bacterial species include genes that are not shared among all strains, this approach avoids the reliance on a common reference genome. However, the same gene can exist in slightly different versions across different strains, leading to diluted effects when trying to detect its association to a phenotype through k-mer based GWAS. Here we propose to overcome this by testing covariates built from closed connected subgraphs of the De Bruijn graph defined over genomic k-mers. These covariates are able to capture polymorphic genes as a single entity, improving k-mer based GWAS in terms of power and interpretability. As the number of subgraphs is exponential in the number of nodes in the DBG, a method naively testing all possible subgraphs would result in very low statistical power due to multiple testing corrections, and the mere exploration of these subgraphs would quickly become computationally intractable. The concept of testable hypothesis has successfully been used to address both problems in similar contexts. We leverage this concept to test all closed connected subgraphs by proposing a novel enumeration scheme for these objects which fully exploits the pruning opportunity offered by testability, resulting in drastic improvements in computational efficiency. We illustrate this on both real and simulated datasets and also demonstrate how considering subgraphs leads to a more powerful and interpretable method. Our method integrates with existing visual tools to facilitate interpretation. We also provide an implementation of our method, as well as code to reproduce all results at https://github.com/HectorRDB/Caldera_Recomb.

genetics↗

bioRxiv2021

Minos: variant adjudication and joint genotyping of cohorts of bacterial genomes

Short-read variant calling for bacterial genomics is a mature field, and there are many widely-used software tools. Different underlying approaches (eg pileup, local or global assembly, paired-read use, haplotype use) lend each tool different strengths, especially when considering non-SNP (single nucleotide polymorphism) variation or potentially distant reference genomes. It would therefore be valuable to be able to integrate the results from multiple variant callers, using a robust statistical approach to "adjudicate" at loci where there is disagreement between callers. To this end, we present a tool, Minos, for variant adjudication by mapping reads to a genome graph of variant calls. Minos allows users to combine output from multiple variant callers without loss of precision. Minos also addresses a second problem of joint genotyping SNPs and indels in bacterial cohorts, which can also be framed as an adjudication problem. We benchmark on 62 samples from 3 species (Mycobacterium tuberculosis, Staphylococcus aureus, Klebsiella pneumoniae) and an outbreak of 385 M. tuberculosis samples. Finally, we joint genotype a large M. tuberculosis cohort (N{approx}15k) for which the rifampicin phenotype is known. We build a map of non-synonymous variants in the RRDR (rifampicin resistance determining region) of the rpoB gene and extend current knowledge relating RRDR SNPs to heterogeneity in rifampicin resistance levels. We replicate this finding in a second M. tuberculosis cohort (N{approx}13k). Minos is released under the MIT license, available at https://github.com/iqbal-lab-org/minos.

bioinformatics↗

bioRxiv2021

Glycoproteogenomics characterizes the CD44 splicing code driving bladder cancer invasion

Bladder cancer (BC) management demands the introduction of novel molecular targets for precision medicine. Cell surface glycoprotein CD44 has been widely studied as a potential biomarker of BC aggressiveness and cancer stem cells. However, significant alternative splicing and multiple glycosylation generate a myriad of glycoproteoforms with potentially distinct functional roles. The lack of tools for precise molecular characterization has led to conflicting results, delaying clinical applications. Addressing these limitations, we have interrogated the transcriptome of a large BC patient cohort for splicing signatures. Remarkable CD44 heterogeneity was observed, as well as associations between short CD44 standard splicing isoform (CD44s), invasion and poor prognosis. In parallel, immunoassays showed that targeting short O-glycoforms could hold the key to improve CD44 cancer specificity. This prompted the development of a glycoproteogenomics approach, building on the integration of transcriptomics-customized datasets and glycomics for protein annotation from nanoLC-ESI-MS/MS experiments. The concept was applied to invasive human BC cell lines, glycoengineered cells, and tumor tissues, enabling unequivocal CD44s identification. Finally, we confirmed the link between CD44s and invasion in vitro by siRNA knockdown, supporting findings from BC tissues. The key role played by short-chain O-glycans in CD44-mediated invasion was also demonstrated through glycoengineered cell models. Overall, CD44s emerged as biomarker of poor prognosis and CD44-Tn/STn as promising molecular signatures for targeted interventions. This study materializes the concept of glycoproteogenomics and provides a key vision to address the cancer splicing code at the protein level, which may now be expanded to better understand CD44 functional role in health and disease. Significance StatementThe biological role of CD44, a cell membrane glycoprotein involved in most cancer hallmarks and widely explored in BC, is intimately linked to its protein isoforms. mRNA alternative splicing generates several closely related polypeptide sequences, which have so far been inferred from transcripts analysis, in the absence of workflows for unequivocal protein annotation. Dense O-glycosylation is also key for protein function and may exponentiate the number of proteoforms, rendering CD44 molecular characterization a daunting enterprise. Here, we integrated multiple molecular information (RNA, proteins, glycans) for definitive CD44 characterization by mass spectrometry, materializing the concept of glycoproteogenomics. BC specific glycoproteoforms linked to invasion have been identified, holding potential for precise cancer targeting. The approach may be transferable to other tumors, paving the way for precision oncology.

cancer biology↗

bioRxiv2021

Exploring bacterial diversity via a curated and searchable snapshot of archived DNA sequences

The open sharing of genomic data provides an incredibly rich resource for the study of bacterial evolution and function, and even anthropogenic activities such as the widespread use of antimicrobials. Whilst these archives are rich in data, considerable processing is required before biological questions can be addressed. Here, we assembled and characterised 661,405 bacterial genomes using a uniform standardised approach, retrieved from the European Nucleotide Archive (ENA) in November of 2018. A searchable COBS index has been produced, facilitating the easy interrogation of the entire dataset for a specific gene or mutation. Additional MinHash and pp-sketch indices support genome-wide comparisons and estimations of genomic distance. An analysis on this scale revealed the uneven species composition in the ENA/public databases, with just 20 of the total 2,336 species making up 90% of the genomes. The over-represented species tend to be acute/common human pathogens. This aligns with research priorities at different levels from individuals with targeted but focused research questions, areas of focus for the funding bodies or national public health agencies, to those identified globally as priority pathogens by the WHO for their resistance to front and last line antimicrobials. Understanding the actual and potential biases in bacterial diversity depicted in this snapshot, and hence within the data being submitted to the public sequencing archives, is essential if we are to target and fill gaps in our understanding of the bacterial kingdom.

microbiology↗

bioRxiv2020

Nucleotide-resolution bacterial pan-genomics with reference graphs

BackgroundBacterial genomes follow a U-shaped frequency distribution whereby most genomic loci are either rare (accessory) or common (core); the union of these is the pan-genome. The alignable fraction of two genomes from a single species can be low (e.g. 50-70%), such that no single reference genome can access all single nucleotide polymorphisms (SNPs). The pragmatic solution is to choose a close reference, and analyse SNPs only in the core genome. Given much bacterial adaptability hinges on the accessory genome, this is an unsatisfactory limitation. ResultsWe present a novel pan-genome graph structure and algorithms implemented in the software pandora, which approximates a sequenced genome as a recombinant of reference genomes, detects novel variation and then pan-genotypes multiple samples. The method takes fastq as input and outputs a multi-sample VCF with respect to an inferred data-dependent reference genome, and is available at https://github.com/rmcolq/pandora. Constructing a reference graph from 578 E. coli genomes, we analyse a diverse set of 20 E. coli isolates. We show pandora recovers at least 13k more rare SNPs than single-reference based tools, achieves equal or better error rates with Nanopore as with Illumina data, 6-24x lower Nanopore error rates than other tools, and provides a stable framework for analysing diverse samples without reference bias. We also show that our inferred recombinant VCF reference genome is significantly better than simply picking the closest RefSeq reference. ConclusionsThis is a step towards comprehensive cohort analysis of bacterial pan-genomic variation, with potential impacts on genotype/phenotype and epidemiological studies.

bioinformatics↗

bioRxiv2020

An integrated multi-omic analysis of iPSC-derived motor neurons from C9ORF72 ALS patients

Neurodegenerative diseases present a challenge for systems biology, due to the lack of reliable animal models and the difficulties in obtaining samples from patients at early stages of disease, when interventions might be most effective. Studying induced pluripotent stem cell (iPSC)-derived neurons could overcome these challenges and dramatically accelerate and broaden therapeutic strategies. Here we undertook a network-based multi-omic characterization of iPSC-derived motor neurons from ALS patients carrying genetically dominant hexanucleotide expansions in C9orf72 to gain a deeper understanding of the relationship between DNA, RNA, epigenetics and protein in the same pool of tissue. ALS motor neurons showed the expected C9orf72-related alterations to specific nucleoporins and production of dipeptide repeats. RNA-seq, ATAC-seq and data-independent acquisition mass-spectrometry (DIA-MS) proteomics were then performed on the same motor neuron cultures. Using integrative computational methods that combined all of the omics, we discovered a number of novel dysregulated pathways including biological adhesion and extracellular matrix organization and disruption in other expected pathways such as RNA splicing and nuclear transport. We tested the relevance of these pathways in vivo in a C9orf72 Drosophila model, analyzing the data to determine which pathways were causing disease phenotypes and which were compensatory. We also confirmed that some pathways are altered in late-stage neurodegeneration by analyzing human postmortem C9 cervical spine data. To validate that these key pathways were integral to the C9 signature, we prepared a separate set of C9orf72 and control motor neuron cultures using a different differentiation protocol and applied the same methods. As expected, there were major overall differences between the differentiation protocols, especially at the level of in individual omics data. However, a number of the core dysregulated pathways remained significant using the integrated multiomic analysis. This new method of analyzing patient specific neural cultures allows the generation of disease-related hypotheses with a small number of patient lines which can be tested in larger cohorts of patients.

neuroscience↗

bioRxiv2019

Chromatin interactome mapping at 139 independent breast cancer risk signals

Genome-wide association studies have identified 196 high confidence independent signals associated with breast cancer susceptibility. Variants within these signals frequently fall in distal regulatory DNA elements that control gene expression. We designed a Capture Hi-C array to enrich for chromatin interactions between the credible causal variants and target genes in six human mammary epithelial and breast cancer cell lines. We show that interacting regions are enriched for open chromatin, histone marks for active enhancers and transcription factors relevant to breast biology. We exploit this comprehensive resource to identify candidate target genes at 139 independent breast cancer risk signals, and explore the functional mechanism underlying altered risk at the 12q24 risk region. Our results demonstrate the power of combining genetics, computational genomics and molecular studies to rationalize the identification of key variants and candidate target genes at breast cancer GWAS signals.

molecular biology↗