Abstract Summary: Currently available bisulfite sequencing tools frequently suffer from low mapping rates and low methylation calls, especially for data generated from the Illumina sequencer, NextSeq. Here, we introduce a sequential trimming-and-retrieving alignment approach for investigating DNA methylation patterns, which significantly improves the number of mapped reads and covered CpG sites. The method is implemented in an automated analysis toolkit for processing bisulfite sequencing reads. Availability and implementation: http://mysbfiles.stonybrook.edu/~xuefenwang/software.html and https://github.com/xfwang/BStools. Contact: [email protected] Supplementary information: Supplementary materials are available at Bioinformatics online.

FamAnn is an automated variant annotation pipeline designed for facilitating target discovery for family-based sequencing studies. It can apply a different inheritance pattern or a de novo mutations discovery model to each family and select single nucleotide variants and small insertions and deletions segregating in each family or shared by multiple families. It also provides a variety of variant annotations and retains and annotates all transcripts hit by a single variant. Excel-compatible outputs including all annotated variants segregating in each family or shared by multiple families will be provided for users to prioritize variants based on their customized thresholds. A list of genes that harbor the segregating variants will be provided as well for possible pathway/network analyses. FamAnn uses the de facto community standard Variant Call Format as the input format and can be applied to whole exome, genome or targeted resequencing data. AVAILABILITY: https://sites.google.com/site/famannotation/home CONTACT: [email protected], [email protected], [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: High throughput parallel sequencing, RNA-Seq, has recently emerged as an appealing alternative to microarray in identifying differentially expressed genes (DEG) between biological groups. However, there still exists considerable discrepancy on gene expression measurements and DEG results between the two platforms. The objective of this study was to compare parallel paired-end RNA-Seq and microarray data generated on 5-azadeoxy-cytidine (5-Aza) treated HT-29 colon cancer cells with an additional simulation study. METHODS: We first performed general correlation analysis comparing gene expression profiles on both platforms. An Errors-In-Variables (EIV) regression model was subsequently applied to assess proportional and fixed biases between the two technologies. Then several existing algorithms, designed for DEG identification in RNA-Seq and microarray data, were applied to compare the cross-platform overlaps with respect to DEG lists, which were further validated using qRT-PCR assays on selected genes. Functional analyses were subsequently conducted using Ingenuity Pathway Analysis (IPA). RESULTS: Pearson and Spearman correlation coefficients between the RNA-Seq and microarray data each exceeded 0.80, with 66%~68% overlap of genes on both platforms. The EIV regression model indicated the existence of both fixed and proportional biases between the two platforms. The DESeq and baySeq algorithms (RNA-Seq) and the SAM and eBayes algorithms (microarray) achieved the highest cross-platform overlap rate in DEG results from both experimental and simulated datasets. DESeq method exhibited a better control on the false discovery rate than baySeq on the simulated dataset although it performed slightly inferior to baySeq in the sensitivity test. RNA-Seq and qRT-PCR, but not microarray data, confirmed the expected reversal of SPARC gene suppression after treating HT-29 cells with 5-Aza. Thirty-three IPA canonical pathways were identified by both microarray and RNA-Seq data, 152 pathways by RNA-Seq data only, and none by microarray data only. CONCLUSIONS: These results suggest that RNA-Seq has advantages over microarray in identification of DEGs with the most consistent results generated from DESeq and SAM methods. The EIV regression model reveals both fixed and proportional biases between RNA-Seq and microarray. This may explain in part the lower cross-platform overlap in DEG lists compared to those in detectable genes.

MOTIVATION: Alternative splicing (AS) is a pre-mRNA maturation process leading to the expression of multiple mRNA variants from the same primary transcript. More than 90% of human genes are expressed via AS. Therefore, quantifying the inclusion level of every exon is crucial for generating accurate transcriptomic maps and studying the regulation of AS. RESULTS: Here we introduce SpliceTrap, a method to quantify exon inclusion levels using paired-end RNA-seq data. Unlike other tools, which focus on full-length transcript isoforms, SpliceTrap approaches the expression-level estimation of each exon as an independent Bayesian inference problem. In addition, SpliceTrap can identify major classes of alternative splicing events under a single cellular condition, without requiring a background set of reads to estimate relative splicing changes. We tested SpliceTrap both by simulation and real data analysis, and compared it to state-of-the-art tools for transcript quantification. SpliceTrap demonstrated improved accuracy, robustness and reliability in quantifying exon-inclusion ratios. CONCLUSIONS: SpliceTrap is a useful tool to study alternative splicing regulation, especially for accurate quantification of local exon-inclusion ratios from RNA-seq data. AVAILABILITY AND IMPLEMENTATION: SpliceTrap can be implemented online through the CSH Galaxy server http://cancan.cshl.edu/splicetrap and is also available for download and installation at http://rulai.cshl.edu/splicetrap/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.