Expression quantitative trait locus (eQTL) mapping is used to identify a functional link between a genomic variant, such as single nucleotide polymorphism (SNP), and gene expression (often close-by pair for cis-eQTL) by linear regression, commonly done when matching genotype and expression data from same individuals are available. Millions of significant eQTLs have been reported by both individual studies and coordinated large consortiums such as Genotype-Tissue Expression project (GTEx). A significant eQTL association does not establish a causal relationship or provide any underlying mechanism so further investigation is needed to understand how a SNP impacts gene expression. One of the plausible explanations for eQTL is that a genomic variant affects transcription factor (TF) binding and thus impacts its regulation on its target genes (TGs). However, data-driven or formal statistical methods to prove that hypothesis are still lacking. To address the gap, we propose a new method called differential co-expression QTL (ceQTL) among different alleles using Chow statistics to specifically detect eQTLs that are modulated by a particular TF. We start with building a trio of TF, its TG, and related SNP and then test the significant coefficient difference among different levels of SNP in terms of TF and TG correlation. We applied this ceQTL model to simulated data and the lung tissue datasets from the GTEx project. The simulated data results showed that the model was robust to detect true ceQTLs at variable sample sizes and different minor allele frequencies as measured by Area Under the Curve (AUC). In normal lung tissue, a small fraction of eQTLs were found to have strong ceQTLs, i.e., eQTLs where SNP affects gene expression though TF binding. Some ceQTLs may not be detected by traditional eQTL analysis. Our tool also performed a TF binding affinity analysis to add another layer of evidence for functional interpretation. Comparisons with other similar tools were also presented. In summary, ceQTL analysis provides a more interpretable and biological insight into the mechanism of eQTL, which would help us better understand how genomic variants affect phenotypes and diseases.

MOTIVATION: Intratumor heterogeneity arises from ongoing somatic evolution and complicates cancer diagnosis, prognosis, and treatment. Reconstructing evolutionary dynamics typically requires spatiotemporal samples, which are often unavailable in clinical settings. Computational approaches that can infer tumor evolutionary history from single-timepoint bulk sequencing data remain limited. RESULTS: We present estimating evolutionary events through single-timepoint sequencing (TEATIME), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric-fitness diversity-which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types. AVAILABILITY AND IMPLEMENTATION: R implementation of TEATIME is available on GitHub (https://github.com/liliulab/TEATIME) and Zenodo (https://zenodo.org/records/17422174).

MCAM (CD146) is a cell surface adhesion molecule that has been reported to promote cancer development, progression and metastasis and is considered as a potential tumor biomarker and therapeutic target. However, inconsistent reports exist, and its clinical value is yet to be confirmed. Here we took advantage of several large genomic data collections (Genotype-Tissue Expression, The Cancer Genome Atlas and Cancer Cell Line Encyclopedia) and comprehensively analyzed MCAM expression in thousands of normal and cancer samples and cell lines along with their clinical phenotypes and drug response information. Our results show that MCAM is very highly expressed in large vessel tissues while majority of tissues have low or minimal expression. Its expression is dramatically increased in a few tumors but significantly decreased in most other tumors relative to their pairing normal tissues. Increased MCAM expression is associated with a higher tumor stage and worse patient survival for some less common tumors but not for major ones. Higher MCAM expression in primary tumors may be complicated by tumor-associated or normal stromal blood vessels yet its significance may differ from the one from cancer cells. MCAM expression is weakly associated with the response to a few small molecular drugs and the association with targeted anti-BRAF agents suggests its involvement in that pathway which warrants further investigation.

BACKGROUND: Emerging evidence suggests retroviruses play a role in the pathophysiology of amyotrophic lateral sclerosis (ALS). Specifically, activation of ancient viral genes embedded in the human genome is theorized to lead to motor neuron degeneration. We explore whether connections exist between ALS and retroviruses through protein interaction networks (PIN) and pathway analysis, and consider the potential roles in drug target discovery. Protein database and pathway/network analytical software including Ingenuity Pathway BioProfiler, STRING, and CytoScape were utilized to identify overlapping protein interaction networks and extract core cluster (s) of retroviruses and ALS. RESULTS: Topological and statistical analysis of the ALS-PIN and retrovirus-PIN identified a shared, essential protein network and a core cluster with significant connections with both networks. The identified core cluster has three interleukin molecules IL10, Il-6 and IL-1B, a central apoptosis regulator TP53, and several major transcription regulators including MAPK1, ANXA5, SQSTM1, SREBF2, and FADD. Pathway enrichment analysis showed that this core cluster is associated with the glucocorticoid receptor singling and neuroinflammation signaling pathways. For confirmation purposes, we applied the same methodology to the West Nile and Polio virus, which demonstrated trivial connectivity with ALS, supporting the unique connection between ALS and retroviruses. CONCLUSIONS: Bioinformatics analysis provides evidence to support pathological links between ALS and retroviral activation. The neuroinflammation and apoptotic regulation pathways are specifically implicated. The continuation and further analysis of large scale genome studies may prove useful in exploring genes important in retroviral activation and ALS, which may help discover new drug targets.

High-throughput bisulfite methylation sequencing such as reduced representation bisulfite sequencing (RRBS), Agilent SureSelect Human Methyl-Seq (Methyl-seq) or whole-genome bisulfite sequencing is commonly used for base resolution methylome research. These data are represented either by the ratio of methylated cytosine versus total coverage at a CpG site or numbers of methylated and unmethylated cytosines. Multiple statistical methods can be used to detect differentially methylated CpGs (DMCs) between conditions, and these methods are often the base for the next step of differentially methylated region identification. The ratio data have a flexibility of fitting to many linear models, but the raw count data take consideration of coverage information. There is an array of options in each datatype for DMC detection; however, it is not clear which is an optimal statistical method. In this study, we systematically evaluated four statistic methods on methylation ratio data and four methods on count-based data and compared their performances with regard to type I error control, sensitivity and specificity of DMC detection and computational resource demands using real RRBS data along with simulation. Our results show that the ratio-based tests are generally more conservative (less sensitive) than the count-based tests. However, some count-based methods have high false-positive rates and should be avoided. The beta-binomial model gives a good balance between sensitivity and specificity and is preferred method. Selection of methods in different settings, signal versus noise and sample size estimation are also discussed.

Driver somatic mutations are a hallmark of a tumor that can be used for diagnosis and targeted therapy. Mutations are primarily detected from tumor DNA. As dynamic molecules of gene activities, transcriptome profiling by RNA sequence (RNA-seq) is becoming increasingly popular, which not only measures gene expression but also structural variations such as mutations and fusion transcripts. Although single-nucleotide variants (SNVs) can be easily identified from RNA-seq, intermediate long insertions/deletions (indels > 2 bases and less than sequence reads) cause significant challenges and are ignored by most RNA-seq analysis tools. This study evaluates commonly used RNA-seq analysis programs along with variant and somatic mutation callers in a series of data sets with simulated and known indels. The aim is to develop strategies for accurate indel detection. Our results show that the RNA-seq alignment is the most important step for indel identification and the evaluated programs have a wide range of sensitivity to map sequence reads with indels, from not at all to decently sensitive. The sensitivity is impacted by sequence read lengths. Most variant calling programs rely on hard evidence indels marked in the alignment and the programs with realignment may use soft-clipped reads for indel inferencing. Based on the observations, we have provided practical recommendations for indel detection when different RNA-seq aligners are used and demonstrated the best option with highly reliable results. With careful customization of bioinformatics algorithms, RNA-seq can be reliably used for both SNV and indel mutation detection that can be used for clinical decision-making.

SUMMARY: The development of the Infinium HumanMethylation450 BeadChip enables epigenome-wide association studies at a reduced cost. One observation of the 450K data is that many CpG sites the beadchip interrogates have very large measurement errors. Including these noisy CpGs will decrease the statistical power of detecting relevant associations due to multiple testing correction. We propose to use intra-class correlation coefficient (ICC), which characterizes the relative contribution of the biological variability to the total variability, to filter CpGs when technical replicates are available. We estimate the ICC based on a linear mixed effects model by pooling all the samples instead of using the technical replicates only. An ultra-fast algorithm has been developed to address the computational complexity and CpG filtering can be completed in minutes on a desktop computer for a 450K data set of over 1000 samples. Our method is very flexible and can accommodate any replicate design. Simulations and a real data application demonstrate that our whole-sample ICC method performs better than replicate-sample ICC or variance-based method. AVAILABILITY AND IMPLEMENTATION: CpGFilter is implemented in R and publicly available under CRAN via the R package 'CpGFilter'. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Reference genome assemblies are subject to change and refinement from time to time. Generally, researchers need to convert the results that have been analyzed according to old assemblies to newer versions, or vice versa, to facilitate meta-analysis, direct comparison, data integration and visualization. Several useful conversion tools can convert genome interval files in browser extensible data or general feature format, but none have the functionality to convert files in sequence alignment map or BigWig format. This is a significant gap in computational genomics tools, as these formats are the ones most widely used for representing high-throughput sequencing data, such as RNA-seq, chromatin immunoprecipitation sequencing, DNA-seq, etc. RESULTS: Here we developed CrossMap, a versatile and efficient tool for converting genome coordinates between assemblies. CrossMap supports most of the commonly used file formats, including BAM, sequence alignment map, Wiggle, BigWig, browser extensible data, general feature format, gene transfer format and variant call format. AVAILABILITY AND IMPLEMENTATION: CrossMap is written in Python and C. Source code and a comprehensive user's manual are freely available at: http://crossmap.sourceforge.net/.

BACKGROUND: Chromatin immunoprecipitation (ChIP) followed by next-generation sequencing (ChIP-Seq) has been widely used to identify genomic loci of transcription factor (TF) binding and histone modifications. ChIP-Seq data analysis involves multiple steps from read mapping and peak calling to data integration and interpretation. It remains challenging and time-consuming to process large amounts of ChIP-Seq data derived from different antibodies or experimental designs using the same approach. To address this challenge, there is a need for a comprehensive analysis pipeline with flexible settings to accelerate the utilization of this powerful technology in epigenetics research. RESULTS: We have developed a highly integrative pipeline, termed HiChIP for systematic analysis of ChIP-Seq data. HiChIP incorporates several open source software packages selected based on internal assessments and published comparisons. It also includes a set of tools developed in-house. This workflow enables the analysis of both paired-end and single-end ChIP-Seq reads, with or without replicates for the characterization and annotation of both punctate and diffuse binding sites. The main functionality of HiChIP includes: (a) read quality checking; (b) read mapping and filtering; (c) peak calling and peak consistency analysis; and (d) result visualization. In addition, this pipeline contains modules for generating binding profiles over selected genomic features, de novo motif finding from transcription factor (TF) binding sites and functional annotation of peak associated genes. CONCLUSIONS: HiChIP is a comprehensive analysis pipeline that can be configured to analyze ChIP-Seq data derived from varying antibodies and experiment designs. Using public ChIP-Seq data we demonstrate that HiChIP is a fast and reliable pipeline for processing large amounts of ChIP-Seq data.

UNLABELLED: TREAT (Targeted RE-sequencing Annotation Tool) is a tool for facile navigation and mining of the variants from both targeted resequencing and whole exome sequencing. It provides a rich integration of publicly available as well as in-house developed annotations and visualizations for variants, variant-hosting genes and host-gene pathways. AVAILABILITY AND IMPLEMENTATION: TREAT is freely available to non-commercial users as either a stand-alone annotation and visualization tool, or as a comprehensive workflow integrating sequencing alignment and variant calling. The executables, instructions and the Amazon Cloud Images of TREAT can be downloaded at the website: http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm.

UNLABELLED: Reduced representation bisulfite sequencing (RRBS) is a cost-effective approach for genome-wide methylation pattern profiling. Analyzing RRBS sequencing data is challenging and specialized alignment/mapping programs are needed. Although such programs have been developed, a comprehensive solution that provides researchers with good quality and analyzable data is still lacking. To address this need, we have developed a Streamlined Analysis and Annotation Pipeline for RRBS data (SAAP-RRBS) that integrates read quality assessment/clean-up, alignment, methylation data extraction, annotation, reporting and visualization. This package facilitates a rapid transition from sequencing reads to a fully annotated CpG methylation report to biological interpretation. AVAILABILITY AND IMPLEMENTATION: SAAP-RRBS is freely available to non-commercial users at the web site http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm.

The shape of ST segment of Electrocardiogram (ECG) is of great importance in diagnosing heart diseases. Based on feature points of ST segments which have been extracted from electrocardiogram (ECG) data with wavelet transform (WT), a five-input-and-single-output adaptive network-based fuzzy interferences system (ANFIS) is designed to classify the shapes of ST segments. In the system the if-then rule of Takagi-Sugeno is taken, and the combination of the gradient descent and the least-squares method is adopted to train the system. The effectiveness is demonstrated via the ECG data from the MIT-BIT and clinical ECG data.