Biology subjects

Flint, J.

Publications and source records attributed to Flint, J..

At least 19 recordsLinked to original sources

bioRxiv2026

A Single-Cell and Spatial 3D Multi-omic Atlas of Developing Human Basal Ganglia and Inhibitory Neurons

The human basal ganglia (BG), subcortical nuclei fundamental to motor regulation and cognitive modulation, is constructed from neurons produced during gestation in the adjacent ganglionic eminences (GEs). GEs are transient structures in the ventral prenatal brain that also generate GABAergic inhibitory neurons which migrate to destinations in the BG, cortex and other destinations. This study aims to elucidate the epigenomic and 3D-genomic dynamics involved in the specification and maturation of GEs and GE-derived neurons, using single-nucleus methyl-3C sequencing (snm3C-seq), highly-multiplexed spatial transcriptomics, and chromatin+RNA single-molecule imaging. Our multi-modal data support a heterogeneous temporal progression across GE subregions, with the lateral GE (LGE) showing declining neurogenic activity in mid-gestation and caudal GE (CGE) exhibiting ongoing developmental progression through infancy. We identified regulatory programs that specify subtypes of BG principal cells, medium spiny neurons (MSN), via synchronized maturation of the 3D-epigenome. In infant brains, we found a transient short-range enriched (SE) chromatin conformation during the transition between oligodendrocyte progenitors (OPCs) and oligodendrocytes (ODCs), and a temporary shift toward Long-range Enriched (LE) chromatin conformation in projection neurons, extending previous works showing the differentiation of neurons and glial cells is associated with permanent SE and LE conformation, respectively. Lastly, we found that gene regulatory regions active in MSNs were enriched in loci associated with genetic risk for neuropsychiatric disease. Our study delineates the highly complex, lineage-specific 3D genomic dynamics in ventral progenitors and basal ganglia populations of the perinatal human brain. HighlightsO_LIJoint 3D genome and DNA methylome analysis of ventral brain progenitor zones C_LIO_LIHeterogeneous developmental progressions of the ganglionic eminences C_LIO_LIDistinct development dynamics and regulatory landscape of MSNs and interneurons C_LIO_LITransient remodeling of the 3D-genome in neurons and oligodendrocyte progenitors C_LI

genomics↗

bioRxiv2025

The mouse pangenome reveals the structural complexity of the murine protein coding landscape

We present the first mouse pangenome consisting of 17 high-quality inbred mouse strain genomes with complete annotation. This collection includes 12 widely used classical laboratory strains and 5 wild-derived strains. We have fully resolved previously incomplete genomic regions, including the major histocompatibility complex (MHC), the defensin cluster, T-cell receptor, and Ly49 complexes. Hundreds of non-reference genes identified in previous publications not found in GRCm39, like Defa1, Raet1a, and Klra20 (Ly49T), were localised in the new reference genomes. We conducted the first genome-wide scan of variable number tandem repeats (VNTRs) within the coding regions of mice, identifying over 400 genes with VNTR polymorphisms up to more than 600 repeat copies and repeat units reaching 990 nucleotides. Our strain-specific annotations enhance RNA-Seq analyses, as demonstrated in PWK/PhJ, where we observed a 5.1% improvement in read mapping and expression level differences in 2.1% of coding genes compared to using GRCm39.

genomics↗

bioRxiv2024

Complementation testing identifies causal genes at quantitative trait loci underlying fear related behavior

Knowing the genes involved in quantitative traits provides a critical entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. Here we address a key step towards that goal by deploying a test that directly queries whether a gene mediates the effect of a quantitative trait locus (QTL). To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six QTLs, and identified six genes. Four genes, Lsamp, Ptprd, Nptx2 and Sh3gl, have known roles in synapse function; the fifth gene, Psip1, is a transcriptional co-activator not previously implicated in behavior; the sixth is a long non-coding RNA 4933413L06Rik with no known function. Single nucleus transcriptomic and epigenetic analyses implicated excitatory neurons as likely mediating the genetic effects. Surprisingly, variation in transcriptome and epigenetic modalities between inbred strains occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results open a bottleneck in using genetic mapping of QTLs to find novel biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.

genetics↗

bioRxiv2023

Peripheral Blood Single-Cell Sequencing Uncovers Common and Specific Immune Aberrations in Fibrotic Lung Diseases

Rationale and ObjectivesThe extent and commonality of peripheral blood immune aberrations in fibrotic interstitial lung diseases are not well characterized. In this study, we aimed to identify common and distinct immune aberrations in patients with idiopathic pulmonary fibrosis (IPF) and fibrotic hypersensitivity pneumonitis (FHP) using cutting-edge single-cell profiling technologies. MethodsSingle-cell RNA sequencing was performed on patients and healthy controls peripheral blood and bronchoalveolar lavage samples using 10X Genomics 5 gene expression and V(D)J profiling. Cell type composition, transcriptional profiles, cellular trajectories and signaling, and T and B cell receptor repertoires were studied. The standard Seurat R pipeline was followed for cell type composition and differential gene expression analyses. Transcription factor activity was imputed using the DoRothEA-VIPER algorithm. Pseudotime analyses were conducted using Monocle3, while RNA velocity analyses were performed with Velocyto, scVelo, and CellRank. Cell-cell connectomics were assessed using the Connectome R package. V(D)J analyses were conducted using CellRanger and Immcantation frameworks. Across all analyses, disease group differences were assessed using the Wilcoxon rank-sum test. Measurements and Main Results327,990 cells from 83 samples were profiled. Overall, changes in monocytes were common to IPF and FHP, whereas lymphocytes exhibited disease-specific aberrations. Both diseases displayed enrichment of CCL3hi/CCL4hi CD14+ monocytes (p<2.2e-16) and S100Ahi CD14+ monocytes (p<2.2e-16) versus controls. Trajectory and RNA velocity analysis suggested that pro-fibrotic macrophages observed in BAL originated from peripheral blood monocytes. Lymphocytes exhibited disease-specific aberrations, with CD8+ GZMKhi T cells and activated B cells primarily enriched in FHP patients. V(D)J analyses revealed unique T and B cell receptor complementarity-determining region 3 (CDR3) amino acid compositions (p<0.05) in FHP and significant IgA enrichment in IPF (p<5.2e-7). ConclusionsWe identified common and disease-specific immune mechanisms in IPF and FHP; S100Ahi monocytes and SPP1hi macrophages are common to IPF and FHP, whereas GMZKhi T lymphocytes and T and B cell receptor repertoires were unique in FHP. Our findings open novel strategies for the diagnosis and treatment of IPF and FHP.

genomics↗

bioRxiv2023

Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing

BackgroundDominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena played a major role in quantitative genetics. However, today most genome-wide association studies (GWAS) assume alleles act additively. MethodsWe systematically investigated both dominance - here representing any non-additive effect - and additivity across 574 physiological and gene expression traits in three mammalian models: a Pig F2 Intercross, a Rat Heterogeneous Stock and a Mouse Heterogeneous Stock. ResultsIn all species, and across all physiological traits, dominance accounts for about one quarter of the heritable variance. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable assuming additivity, we identified 154, 64 and 62 novel dominance QTLs in pigs, rats and mice respectively, that were undetectable as additive QTLs. Similarly, even though most cis-acting eQTLs are additive, we observed a large fraction of dominance variance in gene expression, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, in HS rat transcriptomes, thousands of eQTLs associate with alternate transcripts and exhibit complex additive and dominant architectures, suggesting a mechanism for dominance. ConclusionsAlthough heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.

genetics↗

bioRxiv2023

Phenotypic subtyping via contrastive learning

Defining and accounting for subphenotypic structure has the potential to increase statistical power and provide a deeper understanding of the heterogeneity in the molecular basis of complex disease. Existing phenotype subtyping methods primarily rely on clinically observed heterogeneity or metadata clustering. However, they generally tend to capture the dominant sources of variation in the data, which often originate from variation that is not descriptive of the mechanistic heterogeneity of the phenotype of interest; in fact, such dominant sources of variation, such as population structure or technical variation, are, in general, expected to be independent of subphenotypic structure. We instead aim to find a subspace with signal that is unique to a group of samples for which we believe that subphenotypic variation exists (e.g., cases of a disease). To that end, we introduce Phenotype Aware Components Analysis (PACA), a contrastive learning approach leveraging canonical correlation analysis to robustly capture weak sources of subphenotypic variation. In the context of disease, PACA learns a gradient of variation unique to cases in a given dataset, while leveraging control samples for accounting for variation and imbalances of biological and technical confounders between cases and controls. We evaluated PACA using an extensive simulation study, as well as on various subtyping tasks using genotypes, transcriptomics, and DNA methylation data. Our results provide multiple strong evidence that PACA allows us to robustly capture weak unknown variation of interest while being calibrated and well-powered, far superseding the performance of alternative methods. This renders PACA as a state-of-the-art tool for defining de novo subtypes that are more likely to reflect molecular heterogeneity, especially in challenging cases where the phenotypic heterogeneity may be masked by a myriad of strong unrelated effects in the data. Code AvailabilityPACA is available as an open source R package on GitHub: https://github.com/Adigorla/PACA

genomics↗

bioRxiv2022

Surveillance for SARS-CoV-2 in Ohio's wildlife, companion, and agricultural animals

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in humans in late 2019 and spread rapidly to become a global pandemic. A zoonotic spillover event from animal to human was identified as the presumed origin. Subsequently, reports began emerging regarding spillback events resulting in SARS-CoV-2 infections in multiple animal species. These events highlighted critical links between animal and human health while also raising concerns about the development of new reservoir hosts and potential viral mutations that could alter virulence and transmission or evade immune responses. Characterizing susceptibility, prevalence, and transmission between animal species became a priority to help protect animal and human health. In this study, we coalesced a large team of investigators and community partners to surveil for SARS-CoV-2 in domestic and free-ranging animals around Ohio between May 2020 and August 2021. We focused on species with known or predicted susceptibility to SARS-CoV-2 infection, highly congregated or medically compromised animals (e.g. shelters, barns, veterinary hospitals), and animals that had frequent contact with humans (e.g. pets, agricultural animals, zoo animals, or animals in wildlife hospitals). This included free-ranging deer (n=76), mink (n=57), multiple species of bats (n=65), and other wildlife in addition to domestic cats (n=275) and pigs (n= 184). In total, we tested 800 animals (34 species) via rRT-PCR for SARS-CoV-2 RNA. SARS-CoV-2 viral RNA was not detected in any of the tested animals despite a major peak in human SARS-CoV-2 cases that occurred in Ohio subsequent to the peak of animal samplings. Importantly, due to lack of validated tests for animals, we did not test for SARS-CoV-2 antibodies in this study, which limited our ability to assess exposure. While the results of this study were negative, the surveillance effort was critical and remains key to understanding, predicting, and preventing re-emergence of SARS-CoV-2 in humans or animals.

microbiology↗

bioRxiv2022

A cellular and spatial map of salivary glands at single cell resolution reveals the functional basis of tertiary lymphoid structure formation in Sjogren's syndrome

The key role of tertiary lymphoid structures in autoimmune and non-autoimmune conditions has been recently appreciated. While many of the molecular mechanisms involved in tertiary lymphoid structure (TLS) formation have been identified, their cellular sources and their temporal and spatial relationship to each other during the development of TLS remain unknown. Here we have constructed a cellular and functional map of key components involved in the formation of TLS in the minor salivary glands (SG) in humans. We have confirmed the presence of an immunofibroblast cell state and identified an undescribed immunopericyte cell state with potential immunological functions within TLS. The identification of TLS cellular and functional properties and their relevant modulators provided by this analysis provides key therapeutic cues for TLS associated conditions in autoimmunity and cancer.

immunology↗

bioRxiv2022

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of MDD

Biobanks often contain several phenotypes relevant to a given disorder, and researchers face complex tradeoffs between shallow phenotypes (high sample size, low specificity and sensitivity) and deep phenotypes (low sample size, high specificity and sensitivity). Here, we study an extreme case: Major Depressive Disorder (MDD) in UK Biobank. Previous studies found that shallow and deep MDD phenotypes have qualitatively distinct genetic architectures, but it remains unclear which are optimal for scientific study or clinical prediction. We propose a new framework to get the best of both worlds by integrating together information across hundreds of MDD-relevant phenotypes. First, we use phenotype imputation to increase sample size for the deepest available MDD phenotype, which dramatically improves GWAS power (increases #loci ~10 fold) and PRS accuracy (increases R2 ~2 fold). Further, we show the genetic architecture of the imputed phenotype remains specific to MDD using genetic correlation, PRS prediction in external clinical cohorts, and a novel PRS-based pleiotropy metric. We also develop a complementary approach to improve specificity of GWAS on shallow MDD phenotypes by adjusting for phenome-wide PCs. Finally, we study phenotype integration at the level of GWAS summary statistics, which can increase GWAS and PRS power but introduces non-MDD-specific signals. Our work provides a simple and scalable recipe to improve genetic studies in large biobanks by combining the sample size of shallow phenotypes with the sensitivity and specificity of deep phenotypes.

genetics↗

bioRxiv2022

Deep Learning-based Phenotype Imputation on Population-scale Biobank Data Increases Genetic Discoveries

Biobanks that collect deep phenotypic and genomic data across large numbers of individuals have emerged as a key resource for human genetic research. However, phenotypes acquired as part of Biobanks are often missing across many individuals, limiting the utility of these datasets. The ability to accurately impute or "fill-in" missing phenotypes is critical to harness the power of population-scale Biobank datasets. We propose AutoComplete, a deep learning-based imputation method which can accurately impute missing phenotypes in population-scale Biobank datasets. When applied to collections of phenotypes measured across {approx} 300K individuals from the UK Biobank, AutoComplete improved imputation accuracy over existing 2 methods (average improvement in r2 of 18% for all phenotypes and 42% for binary phenotypes). We explored the utility of phenotype imputation for improving the power of genome-wide association studies (GWAS) by applying our method to a group of five clinically relevant traits with an average missigness rate of 83% (67% to 94%) leading to an an increase in effective sample size of {approx}2-fold on average (0.5 to 3.3-fold across the phenotypes). GWAS on the resulting imputed phenotypes led to an increase in the total number of loci significantly associated to the traits from four to 129. Our results demonstrate the utility of deep-learning based imputation to increase power for genetic discoveries in existing biobank data sets.

bioinformatics↗

bioRxiv2022

Genetic architecture of creativity and extensive genetic overlap with psychiatric disorders revealed from genome-wide association analyses of 241,736 individuals

Creativity is heritable and exhibits familial aggregation with psychiatric disorders, but its genomic basis and genetic relationship with psychiatric disorders remain largely unknown. Here, we conducted a genome-wide association study (GWAS) using an expanded, machine learning-based definition of creativity in individuals of European ancestry from the UK Biobank (n = 241,736) and identified 25 creativity-associated loci. Extensive genetic overlap with psychiatric disorders, including schizophrenia, major depression, bipolar I disorder, attention deficit/hyperactivity disorder, and anorexia nervosa, was demonstrated by the genetic correlation, polygenic risk score, and MiXeR analyses. The condFDR and conjFDR analyses identified additional loci for creativity and psychiatric disorders, as well as shared genetic loci between creativity and psychiatric disorders. This GWAS showed significant correlations with GWASs using traditional definitions of creativity and GWASs adjusted for educational attainment. Our findings contribute to the understanding of the genetic architecture of creativity and reveal its polygenic relationships with psychiatric disorders.

genomics↗

bioRxiv2022

Cross-trait assortative mating is widespread and inflates genetic correlation estimates

The observation of genetic correlations between disparate traits has been interpreted as evidence of widespread pleiotropy, altered theories of human genetic architecture, and spurred considerable research activity across the natural and social sciences. Here, we introduce cross-trait assortative mating (xAM) as an alternative explanation for observed genetic correlations. We observe that xAM is common across a broad array of phenotypes and that phenotypic cross-mate correlation estimates are strongly associated with genetic correlation estimates (R2 = 76%). Then, we present theoretical and simulation-based results demonstrating that, under xAM, genetic correlation estimators yield significant estimates even for traits with entirely distinct genetic bases. We demonstrate that existing xAM plausibly accounts for substantial fractions of genetic correlation estimates in two large samples (N = 827,960). For example, previously reported genetic correlation estimates between many pairs of psychiatric disorders are fully consistent with xAM alone. Finally, we provide evidence for a history of xAM at the genetic level using a novel approach based on cross-trait even/odd chromosome polygenic score correlations. Together, our results demonstrate that previous reports have likely overestimated the true genetic similarity between many phenotypes.

genetics↗

bioRxiv2021

Analysis of independent cohorts of outbred CFW mice reveals novel loci for behavioral and physiological traits and identifies factors determining reproducibility

Combining samples for genetic association is standard practice in human genetic analysis of complex traits, but is rarely undertaken in rodent genetics. Here, using 23 phenotypes and genotypes from two independent laboratories, we obtained a sample size of 3,076 commercially available outbred mice and identified 70 loci, more than double the number of loci identified in the component studies. Fine-mapping in the combined sample reduced the number of likely causal variants, with a median reduction in set size of 51%, and indicated novel gene associations, including Pnpo, Ttll6 and GM11545 with bone mineral density, and Psmb9 with weight. However replication at a nominal threshold of 0.05 between the two component studies was surprisingly low, with less than a third of loci identified in one study replicated in the second. In addition to overestimates in the effect size in the discovery sample (Winners Curse), we also found that heterogeneity between studies explained the poor replication, but the contribution of these two factors varied among traits. Available methods to control Winners Curse were contingent on the power of the discovery sample, and depending on the method used, both overestimated and underestimated the true effect. Leveraging these observations we integrated information about replication rates, confounding, and Winners Curse corrected estimates of power to assign variants to one of four confidence levels. Our approach addresses concerns about reproducibility, and demonstrates how to obtain robust results from mapping complex traits in any genome-wide association study.

bioinformatics↗

bioRxiv2020

Rapid cost-effective viral genome sequencing by V-seq

Conventional methods for viral genome sequencing largely use metatranscriptomic approaches or, alternatively, enrich for viral genomes by amplicon sequencing with virus-specific PCR or hybridization-based capture. These existing methods are costly, require extensive sample handling time, and have limited throughput. Here, we describe V-seq, an inexpensive, fast, and scalable method that performs targeted viral genome sequencing by multiplexing virus-specific primers at the cDNA synthesis step. We designed densely tiled reverse transcription (RT) primers across the SARS-CoV-2 genome, with a subset of hexamers at the 3 end to minimize mis-priming from the abundant human rRNA repeats and human RNA PolII transcriptome. We found that overlapping RT primers do not interfere, but rather act in concert to improve viral genome coverage in samples with low viral load. We provide a path to optimize V-seq with SARS-CoV-2 as an example. We anticipate that V-seq can be applied to investigate genome evolution and track outbreaks of RNA viruses in a cost-effective manner. More broadly, the multiplexed RT approach by V-seq can be generalized to other applications of targeted RNA sequencing.

genomics↗

bioRxiv2020

A comprehensive benchmarking of WGS-based structural variant callers

Advances in whole genome sequencing promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from whole genome sequencing (WGS) data presents a substantial number of challenges and a plethora of SV-detection methods have been developed. Currently, there is a paucity of evidence which investigators can use to select appropriate SV-detection tools. In this paper, we evaluated the performance of SV-detection tools using a comprehensive PCR-confirmed gold standard set of SVs. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of SV-detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance, as the SV-detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV-detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low and ultra-low pass sequencing data.

bioinformatics↗

bioRxiv2019

Leveraging eQTLs to identify individual-level tissue of interest for a complex trait

Genetic predisposition for complex traits often acts through multiple tissues at different time points during development. As a simple example, the genetic predisposition for obesity could be manifested either through inherited variants that control metabolism through regulation of genes expressed in the brain, or that control fat storage through dysregulation of genes expressed in adipose tissue, or both. Here we describe a statistical approach that leverages tissue-specific expression quantitative trait loci (eQTLs) corresponding to tissue-specific genes to prioritize a relevant tissue underlying the genetic predisposition of a given individual for a complex trait. Unlike existing approaches that prioritize relevant tissues for the trait in the population, our approach probabilistically quantifies the tissue-wise genetic contribution to the trait for a given individual. We hypothesize that for a subgroup of individuals the genetic contribution to the trait can be mediated primarily through a specific tissue. Through simulations using the UK Biobank, we show that our approach can predict the relevant tissue accurately and can cluster individuals according to their tissue-specific genetic architecture. We analyze body mass index (BMI) and waist to hip ratio adjusted for BMI (WHRadjBMI) in the UK Biobank to identify subgroups of individuals whose genetic predisposition act primarily through brain versus adipose tissue, and adipose versus muscle tissue, respectively. Notably, we find that these individuals have specific phenotypic features beyond BMI and WHRadjBMI that distinguish them from random individuals in the data, suggesting biological effects of tissue-specific genetic contribution for these traits.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genetics↗

bioRxiv2018

DISTMIX2: robust imputation of summary statistics for cosmopolitan cohorts using a large and diverse reference panel

Genotype imputation across populations of mixed ancestry is critical for optimal discovery in large-scale genome-wide association studies (GWAS). Methods for direct imputation of GWAS summary statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage-disequilibrium (LD) and that most of the methods using a small reference panel e.g., ~2,500 subject coming from the 1000 Genome Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: i) uses a much larger and more diverse reference panel and ii) estimates weights of ethnic mixture based solely on Z-scores (when AFs are not available). We applied DISTMIX2 to GWAS summary statistics from the Psychiatric Genetic Consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post-traumatic stress disorder (PTSD) study found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most Psychiatric GWAS studies.

bioinformatics↗

bioRxiv2018

A comprehensive analysis of the usability and archival stability of omics computational tools and resources

Developing new software tools for analysis of large-scale biological data is a key component of advancing modern biomedical research. Scientific reproduction of published findings requires running computational tools on data generated by such studies, yet little attention is presently allocated to the installability and archival stability of computational software tools. Scientific journals require data and code sharing, but none currently require authors to guarantee the continuing functionality of newly published tools. We have estimated the archival stability of computational biology software tools by performing an empirical analysis of the internet presence for 36,702 omics software resources published from 2005 to 2017. We found that almost 28% of all resources are currently not accessible through URLs published in the paper they first appeared in. Among the 98 software tools selected for our installability test, 51% were deemed \"easy to install,\" and 28% of the tools failed to be installed at all due to problems in the implementation. Moreover, for papers introducing new software, we found that the number of citations significantly increased when authors provided an easy installation process. We propose for incorporation into journal policy several practical solutions for increasing the widespread installability and archival stability of published bioinformatics software.

bioinformatics↗