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BackgroundMachine learning (ML) methodology development for the classification of immune states in adaptive immune receptor repertoires (AIRRs) has seen a recent surge of interest. However, so far, there does not exist a systematic evaluation of scenarios where classical ML methods (such as penalized logistic regression) already perform adequately for AIRR classification. This hinders investigative reorientation to those scenarios where method development of more sophisticated ML approaches may be required.ResultsTo identify those scenarios where a baseline ML method is able to perform well for AIRR classification, we generated a collection of synthetic AIRR benchmark data sets encompassing a wide range of data set architecture-associated and immune state–associated sequence patterns (signal) complexity. We trained ≈1,700 ML models with varying assumptions regarding immune signal on ≈1,000 data sets with a total of ≈250,000 AIRRs containing ≈46 billion TCRβ CDR3 amino acid sequences, thereby surpassing the sample sizes of current state-of-the-art AIRR-ML setups by two orders of magnitude. We found that L1-penalized logistic regression achieved high prediction accuracy even when the immune signal occurs only in 1 out of 50,000 AIR sequences.ConclusionsWe provide a reference benchmark to guide new AIRR-ML classification methodology by (i) identifying those scenarios characterized by immune signal and data set complexity, where baseline methods already achieve high prediction accuracy, and (ii) facilitating realistic expectations of the performance of AIRR-ML models given training data set properties and assumptions. Our study serves as a template for defining specialized AIRR benchmark data sets for comprehensive benchmarking of AIRR-ML methods.

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. So far, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency and interoperability. immuneML ( immuneml.uio.no ) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (1) reproducing a large-scale study on immune state prediction, (2) developing, integrating and applying a novel deep learning method for antigen specificity prediction and (3) showcasing streamlined interpretability-focused benchmarking of AIRR ML. The proliferation of molecular biology and bioinformatics tools necessary to generate huge quantities of immune receptor data has not been matched by frameworks that allow easy data analysis. The authors present immuneML, an open-source collaborative ecosystem for machine learning analysis of adaptive immune receptor repertoires.

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

Functional genomics assays produce sets of genomic regions as one of their main outputs. To biologically interpret such region-sets, researchers often use colocalization analysis, where the statistical significance of colocalization (overlap, spatial proximity) between two or more region-sets is tested. Existing colocalization analysis tools vary in the statistical methodology and analysis approaches, thus potentially providing different conclusions for the same research question. As the findings of colocalization analysis are often the basis for follow-up experiments, it is helpful to use several tools in parallel and to compare the results. We developed the Coloc-stats web service to facilitate such analyses. Coloc-stats provides a unified interface to perform colocalization analysis across various analytical methods and method-specific options (e.g. colocalization measures, resolution, null models). Coloc-stats helps the user to find a method that supports their experimental requirements and allows for a straightforward comparison across methods. Coloc-stats is implemented as a web server with a graphical user interface that assists users with configuring their colocalization analyses. Coloc-stats is freely available at https://hyperbrowser.uio.no/coloc-stats/.

Autoantibodies to transglutaminase 2 (TG2) are hallmarks of celiac disease. To address B cell tolerance and autoantibody formation to TG2, we generated immunoglobulin knock-in (Ig KI) mice that express a prototypical celiac patient–derived anti-TG2 B cell receptor equally reactive to human and mouse TG2. We studied B cell development in the presence/absence of autoantigen by crossing the Ig KI mice to Tgm2−/− mice. Autoreactive B cells in Tgm2+/+ mice were indistinguishable from their naive counterparts in Tgm2−/− mice with no signs of clonal deletion, receptor editing, or B cell anergy. The autoreactive B cells appeared ignorant to their antigen, and they produced autoantibodies when provided T cell help. The findings lend credence to a model of celiac disease where gluten-reactive T cells provide help to autoreactive TG2-specific B cells by involvement of gluten–TG2 complexes, and they outline a general mechanism of autoimmunity with autoantibodies being produced by ignorant B cells on provision of T cell help.

Plasma cells (PCs) are terminally differentiated B-lymphocytes producing antibodies. In coeliac disease (CeD) there is increased density of PCs in the small-intestinal lesion. Many of these PCs produce disease-specific autoantibodies targeting transglutaminase 2 (TG2).The plasmacytosis of CeD motivated us to study the transcriptional programme of PCs from coeliac gut lesions.RNA-seq was performed on the PCs of CeD patients and disease controls, being specific or non-specific for TG2.Being antibody-producing cells, 67% of the PCs' transcript was aligned to immunoglobulin genes. Strikingly, genes encoding ligands and receptors of chemokines and cytokines were abundant. Higher transcript levels of genes associated with cell activation and immune responses were observed in PCs of CeD patients compared to controls. TG2-specific compared to non-TG2 specific PCs expressed increased levels of CXCR3, CXCL10 and interleukin-15; factors that have been implicated in the pathogenesis of CeD yet with production attributed to other cells than PCs. The presence of transcripts of HLA class II and T-cell co-stimulatory molecules suggests that PCs may serve as antigen-presenting cells for CD4 + helper T cells.Our findings shed new light on the biology of intestinal PCs, implicating functions that go beyond the production of immunoglobulins.

Combining HLA-DQ-gluten tetramers with mass cytometry and RNA sequencing analysis, we find that gluten-specific CD4+ T cells in the blood and intestines of patients with celiac disease display a surprisingly rare phenotype. Cells with this phenotype are also elevated in patients with systemic sclerosis and systemic lupus erythematosus, suggesting a way to characterize CD4+ T cells specific for disease-driving antigens in multiple autoimmune conditions.

BackgroundThe difficulties associated with sequencing and assembling some regions of the DNA sequence result in gaps in the reference genomes that are typically represented as stretches of Ns. Although the presence of assembly gaps causes a slight reduction in the mapping rate in many experimental settings, that does not invalidate the typical statistical testing comparing read count distributions across experimental conditions. However, we hypothesize that not handling assembly gaps in the null model may confound statistical testing of co-localization of genomic features.ResultsFirst, we performed a series of explorative analyses to understand whether and how the public genomic tracks intersect the assembly gaps track (hg19). The findings rightly confirm that the genomic regions in public genomic tracks intersect very little with assembly gaps and the intersection was observed only at the beginning and end regions of the assembly gaps rather than covering the whole gap sizes. Further, we simulated a set of query and reference genomic tracks in a way that nullified any dependence between them to test our hypothesis that not avoiding assembly gaps in the null model would result in spurious inflation of statistical significance. We then contrasted the distributions of test statistics and p-values of Monte Carlo simulation-based permutation tests that either avoided or not avoided assembly gaps in the null model when testing for significant co-localization between a pair of query and reference tracks. We observed that the statistical tests that did not account for the assembly gaps in the null model resulted in a distribution of the test statistic that is shifted to the right and a distribu tion of p-values that is shifted to the left (leading to inflated significance).ConclusionOur results shows that not accounting for assembly gaps in statistical testing of co-localization analysis may lead to false positives and over-optimistic findings.

Distribution of the observed test statistic and the average test statistic under the null model for DNase I hypersensitive site (hg19). Distribution of the test statistic and p-values of colocalization analysis for a collection of 838 genomic tracks with 281.47 bp average segment length. (a) and (b) shows the distribution of p-values of the colocalization analysis with (left) and without (right) exclusion of assembly gap regions under the null model. (c) and (d) shows the observed test statistic and the average test statistic of the same tracks with (left) and without (right) exclusion of assembly gap regions under the null model. (PDF 70 kb)

Distribution of the observed test statistic and the average test statistic under the null model for DNase I hypersensitive sites (hg38). Distribution of the test statistic and p-values of colocalization analysis for a collection of 95 genomic tracks with 150.35 bp average segment length. (a) and (b) shows the distribution of p-values of the colocalization analysis with (left) and without (right) exclusion of assembly gap regions under the null model. (c) and (d) shows the observed test statistic and the average test statistic of the same tracks with (left) and without (right) exclusion of assembly gap regions under the null model. (PDF 72 kb)

The aryl hydrocarbon receptor (AHR) mediates the toxic effects of dioxin (2, 3, 7, 8-tetrachlorodibenzo-p-dioxin; TCDD), which includes thymic atrophy, steatohepatitis, and a lethal wasting syndrome in laboratory rodents. Although the mechanisms of dioxin toxicity remain unknown, AHR signaling in hepatocytes is necessary for dioxin-induced liver toxicity. We previously reported that loss of TCDD-inducible poly(adenosine diphosphate [ADP]-ribose) polymerase (TIPARP/PARP7/ARTD14), an AHR target gene and mono-ADP-ribosyltransferase, increases the sensitivity of mice to dioxin-induced toxicities. To test the hypothesis that TIPARP is a negative regulator of AHR signaling in hepatocytes, we generated Tiparp(fl/fl) mice in which exon 3 of Tiparp is flanked by loxP sites, followed by Cre-lox technology to create hepatocyte-specific (Tiparp(fl/fl)Cre(Alb)) and whole-body (Tiparp(fl/fl)Cre(CMV); Tiparp(Ex3−/−)) Tiparp null mice. Tiparp(fl/fl)Cre(Alb) and Tiparp(Ex3−/−) mice given a single injection of 10 μg/kg dioxin did not survive beyond days 7 and 9, respectively, while all Tiparp(+/+) mice survived the 30-day treatment. Dioxin-exposed Tiparp(fl/fl)Cre(Alb) and Tiparp(Ex3−/−) mice had increased steatohepatitis and hepatotoxicity as indicated by greater staining of neutral lipids and serum alanine aminotransferase activity than similarly treated wild-type mice. Tiparp(fl/fl)Cre(Alb) and Tiparp(Ex3−/−) mice exhibited augmented AHR signaling, denoted by increased dioxin-induced gene expression. Metabolomic studies revealed alterations in lipid and amino acid metabolism in liver extracts from Tiparp(fl/fl)Cre(Alb) mice compared with wild-type mice. Taken together, these data illustrate that TIPARP is an important negative regulator of AHR activity, and that its specific loss in hepatocytes is sufficient to increase sensitivity to dioxin-induced steatohepatitis and lethality.

Distribution of the observed test statistic and the average test statistic under the null model for TFBS in K562 (hg19). Distribution of the test statistic and p-values of colocalization analysis for a collection of 568 genomic tracks with 3850.15 bp average segment length. (a) and (b) shows the distribution of p-values of the colocalization analysis with (left) and without (right) exclusion of assembly gap regions under the null model. (c) and (d) shows the observed test statistic and the average test statistic of the same tracks with (left) and without (right) exclusion of assembly gap regions under the null model. (PDF 69 kb)

Overlap of public genomic tracks with genome assembly gaps of hg19 and hg38. Overlap of public genomic tracks with assembly gaps and average segment length of the tracks (a) for DNase I hypersensitive sites (hg19) (b) for histone modifications (hg19) (c) for TFBS in K562 (hg19) (d) for DNase I hypersensitive sites (hg38). Overlap of public genomic tracks with assembly gaps increased with an increase in the average segment length of the tracks. (PDF 60 kb)

Combining HLA-DQ-gluten tetramers with mass cytometry and RNA sequencing analysis, we find that gluten-specific CD4

Here we characterized the genome-wide architecture of copy number variations (CNVs) in 286 healthy, unrelated Finnish individuals belonging to the MUSGEN study, where molecular background underlying musical aptitude and related traits are studied. By using Illumina HumanOmniExpress-12v.1.0 beadchip, we identified 5493 CNVs that were spread across 467 different cytogenetic regions, spanning a total size of 287.83 Mb (∼9.6% of the human genome). Merging the overlapping CNVs across samples resulted in 999 discrete copy number variable regions (CNVRs), of which ∼6.9% were putatively novel. The average number of CNVs per person was 20, whereas the average size of CNV per locus was 52.39 kb. Large CNVs (1 Mb) were present in 4% of the samples. The proportion of homozygous deletions in this data set (∼12.4%) seemed to be higher when compared with three other populations. Interestingly, several CNVRs were significantly enriched in this sample set, whereas several others were totally depleted. For example, a CNVR at chr2p22.1 intersecting GALM was more common in this population (P=3.3706 × 10(-44)) than in African and other European populations. The enriched CNVRs, however, showed no significant association with music-related phenotypes. Moreover, the most common CNV locations in world's normal population cohorts (6q14.1, 11q11) were overrepresented in this population. Thus, the genome-wide CNV investigation in this Finnish sample set demonstrated features that are characteristic to isolated populations. Novel CNVRs and the functional implications of CNVs revealed in this study elucidate structural variation present in this population isolate, and may also serve as candidate gene loci for music-related traits.

Genomic locations are represented as coordinates on a specific genome build version, but the build information is frequently missing when coordinates are provided. It is essential to correctly interpret and analyse the genomic intervals contained in genomic track files. Here, we demonstrate that this crucial metadatum (or rather datum) is often isolated from the genomic track files in public repositories and journal articles, which could be a major time thief. We propose best practices to ensure that genome build version is always carried along with genomic track files. Although not a substitute to the best practices, we also provide a tool to predict the genome build version of genomic track files.

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To obtain aggregate evidence for the molecular basis of musical abilities and the effects of music, we integrated gene-level data from 105 published studies across multiple species including humans, songbirds and several other animals and used a convergent evidence method to prioritize the top candidate genes. Several of the identified top candidate genes like EGR1, FOS, ARC, BDNF and DUSP1 are known to be activity-dependent immediate early genes that respond to sensory and motor stimuli in the brain. Several other top candidate genes like MAPK10, SNCA, ARHGAP24, TET2, UBE2D3, FAM13A and NUDT9 are located on chromosome 4q21-q24, on the candidate genomic region for music abilities in humans. Functional annotation analyses showed the enrichment of genes involved in functions like cognition, learning, memory, neuronal excitation and apoptosis, long-term potentiation and CDK5 signaling pathway. Interestingly, all these biological functions are known to be essential processes underlying learning and memory that are also fundamental for musical abilities including recognition and production of sound. In summary, our study prioritized top candidate genes related to musical traits.

To obtain aggregate evidence for the molecular basis of musical abilities and the effects of music, we integrated gene-level data from 101 published studies across multiple species including humans, songbirds and several other animals and used a convergent evidence method to prioritize the top candidate genes. Several of the identified top candidate genes like EGR1, FOS, ARC, BDNF and DUSP1 are known to be activity-dependent immediate early genes that respond to sensory and motor stimuli in the brain. Several other top candidate genes like MAPK10, SNCA, ARHGAP24, TET2, UBE2D3, FAM13A and NUDT9 are located on chromosome 4q21-q24, on the candidate genomic region for music abilities in humans. Functional annotation analyses showed the enrichment of genes involved in functions like cognition, learning, memory, neuronal excitation and apoptosis, long-term potentiation and CDK5 signaling pathway. Interestingly, all these biological functions are known to be essential processes underlying learning and memory that are also fundamental for musical abilities including recognition and production of sound. In summary, our study prioritized top candidate genes related to musical traits that are possibly conserved through evolution, as suggested by shared molecular background with other species.

Summary:Modern high-throughput studies often yield long lists of genes, of which a fraction are of high relevance to the phenotype of interest. To prioritize the candidate genes of complex genetic traits, our R/Bioconductor package GenRank provides methods that are based on convergent evidence obtained from multiple independent evidence layers. The package facilitates an extensible framework that allows a further addition of novel methods for candidate gene prioritization.Availability and Implementation:The methods are implemented in R and available as a package in Bioconductor repository (http://bioconductor.org/packages/GenRank/).Contact:chakra.kanduri@gmail.com

Abilities related to musical aptitude appear to have a long history in human evolution. To elucidate the molecular and evolutionary background of musical aptitude, we compared genome-wide genotyping data (641 K SNPs) of 148 Finnish individuals characterized for musical aptitude. We assigned signatures of positive selection in a case-control setting using three selection methods: haploPS, XP-EHH and FST. Gene ontology classification revealed that the positive selection regions contained genes affecting inner-ear development. Additionally, literature survey has shown that several of the identified genes were known to be involved in auditory perception (e.g. GPR98, USH2A), cognition and memory (e.g. GRIN2B, IL1A, IL1B, RAPGEF5), reward mechanisms (RGS9), and song perception and production of songbirds (e.g. FOXP1, RGS9, GPR98, GRIN2B). Interestingly, genes related to inner-ear development and cognition were also detected in a previous genome-wide association study of musical aptitude. However, the candidate genes detected in this study were not reported earlier in studies of musical abilities. Identification of genes related to language development (FOXP1 and VLDLR) support the popular hypothesis that music and language share a common genetic and evolutionary background. The findings are consistent with the evolutionary conservation of genes related to auditory processes in other species and provide first empirical evidence for signatures of positive selection for abilities that contribute to musical aptitude.

Although brain imaging studies have demonstrated that listening to music alters human brain structure and function, the molecular mechanisms mediating those effects remain unknown. With the advent of genomics and bioinformatics approaches, these effects of music can now be studied in a more detailed fashion. To verify whether listening to classical music has any effect on human transcriptome, we performed genome-wide transcriptional profiling from the peripheral blood of participants after listening to classical music (n = 48), and after a control study without music exposure (n = 15). As musical experience is known to influence the responses to music, we compared the transcriptional responses of musically experienced and inexperienced participants separately with those of the controls. Comparisons were made based on two subphenotypes of musical experience: musical aptitude and music education. In musically experiencd participants, we observed the differential expression of 45 genes (27 up- and 18 down-regulated) and 97 genes (75 up- and 22 down-regulated) respectively based on subphenotype comparisons (rank product non-parametric statistics, pfp 0.05, >1.2-fold change over time across conditions). Gene ontological overrepresentation analysis (hypergeometric test, FDR < 0.05) revealed that the up-regulated genes are primarily known to be involved in the secretion and transport of dopamine, neuron projection, protein sumoylation, long-term potentiation and dephosphorylation. Down-regulated genes are known to be involved in ATP synthase-coupled proton transport, cytolysis, and positive regulation of caspase, peptidase and endopeptidase activities. One of the most up-regulated genes, alpha-synuclein (SNCA), is located in the best linkage region of musical aptitude on chromosome 4q22.1 and is regulated by GATA2, which is known to be associated with musical aptitude. Several genes reported to regulate song perception and production in songbirds displayed altered activities, suggesting a possible evolutionary conservation of sound perception between species. We observed no significant findings in musically inexperienced participants.

Modern high-throughput studies often yield long lists of genes, a fraction of which are of high relevance to the phenotype of interest. To prioritize the candidate genes of complex genetic traits, our R/Bioconductor package GenRank ranks genes based on convergent evidence obtained from multiple layers of independent evidence. We implemented three methods to rank genes that integrate gene-level data generated from multiple layers of evidence: (a) the convergent evidence (CE) method aggregates evidence based on a weighted vote counting method; (b) the rank product (RP) method performs a meta-analysis of microarray-based gene expression data, and (c) the traditional method combines p-values. The methods are implemented in R and are available as a package in the Bioconductor repository (http://bioconductor.org/packages/GenRank/).

Music performance by professional musicians involves a wide-spectrum of cognitive and multi-sensory motor skills, whose biological basis is unknown. Several neuroscientific studies have demonstrated that the brains of professional musicians and non-musicians differ structurally and functionally and that musical training enhances cognition. However, the molecules and molecular mechanisms involved in music performance remain largely unexplored. Here, we investigated the effect of music performance on the genome-wide peripheral blood transcriptome of professional musicians by analyzing the transcriptional responses after a 2-hr concert performance and after a ‘music-free’ control session. The up-regulated genes were found to affect dopaminergic neurotransmission, motor behavior, neuronal plasticity and neurocognitive functions including learning and memory. Particularly, candidate genes such as SNCA, FOS and DUSP1 that are involved in song perception and production in songbirds, were identified, suggesting an evolutionary conservation in biological processes related to sound perception/production. Additionally, modulation of genes related to calcium ion homeostasis, iron ion homeostasis, glutathione metabolism and several neuropsychiatric and neurodegenerative diseases implied that music performance may affect the biological pathways that are otherwise essential for the proper maintenance of neuronal function and survival. For the first time, this study provides evidence for the candidate genes and molecular mechanisms underlying music performance.

Music perception and practice represent complex cognitive functions of the human brain. Recently, evidence for the molecular genetic background of music related phenotypes has been obtained. In order to further elucidate the molecular background of musical phenotypes we analyzed genome wide copy number variations (CNVs) in five extended pedigrees and in 172 unrelated subjects characterized for musical aptitude and creative functions in music. Musical aptitude was defined by combination of the scores of three music tests (COMB scores): auditory structuring ability, Seashores test for pitch and for time. Data on creativity in music (herein composing, improvising and/or arranging music) was surveyed using a web-based questionnaire. Several CNVRs containing genes that affect neurodevelopment, learning and memory were detected. A deletion at 5q31.1 covering the protocadherin-α gene cluster (Pcdha 1-9) was found co-segregating with low music test scores (COMB) in both sample sets. Pcdha is involved in neural migration, differentiation and synaptogenesis. Creativity in music was found to co-segregate with a duplication covering glucose mutarotase gene (GALM) at 2p22. GALM has influence on serotonin release and membrane trafficking of the human serotonin transporter. Interestingly, genes related to serotonergic systems have been shown to associate not only with psychiatric disorders but also with creativity and music perception. Both, Pcdha and GALM, are related to the serotonergic systems influencing cognitive and motor functions, important for music perception and practice. Finally, a 1.3 Mb duplication was identified in a subject with low COMB scores in the region previously linked with absolute pitch (AP) at 8q24. No differences in the CNV burden was detected among the high/low music test scores or creative/non-creative groups. In summary, CNVs and genes found in this study are related to cognitive functions. Our result suggests new candidate genes for music perception related traits and supports the previous results from AP study.

Motivation Many high-throughput methods produce sets of genomic regions as one of their main outputs. Scientists often use genomic colocalization analysis to interpret such region sets, for example to identify interesting enrichments and to understand the interplay between the underlying biological processes. Although widely used, there is little standardization in how these analyses are performed. Different practices can substantially affect the conclusions of colocalization analyses. Results Here, we describe the different approaches and provide recommendations for performing genomic colocalization analysis, while also discussing common methodological challenges that may influence the conclusions. As illustrated by concrete example cases, careful attention to analysis details is needed in order to meet these challenges and to obtain a robust and biologically meaningful interpretation of genomic region set data. Supplementary information Supplementary data are available at Bioinformatics online.