BackgroundSome applications, especially those clinical applications requiring high accuracy of sequencing data, usually have to face the troubles caused by unavoidable sequencing errors. Several tools have been proposed to profile the sequencing quality, but few of them can quantify or correct the sequencing errors. This unmet requirement motivated us to develop AfterQC, a tool with functions to profile sequencing errors and correct most of them, plus highly automated quality control and data filtering features. Different from most tools, AfterQC analyses the overlapping of paired sequences for pair-end sequencing data. Based on overlapping analysis, AfterQC can detect and cut adapters, and furthermore it gives a novel function to correct wrong bases in the overlapping regions. Another new feature is to detect and visualise sequencing bubbles, which can be commonly found on the flowcell lanes and may raise sequencing errors. Besides normal per cycle quality and base content plotting, AfterQC also provides features like polyX (a long sub-sequence of a same base X) filtering, automatic trimming and K-MER based strand bias profiling.ResultsFor each single or pair of FastQ files, AfterQC filters out bad reads, detects and eliminates sequencer’s bubble effects, trims reads at front and tail, detects the sequencing errors and corrects part of them, and finally outputs clean data and generates HTML reports with interactive figures. AfterQC can run in batch mode with multiprocess support, it can run with a single FastQ file, a single pair of FastQ files (for pair-end sequencing), or a folder for all included FastQ files to be processed automatically. Based on overlapping analysis, AfterQC can estimate the sequencing error rate and profile the error transform distribution. The results of our error profiling tests show that the error distribution is highly platform dependent.ConclusionMuch more than just another new quality control (QC) tool, AfterQC is able to perform quality control, data filtering, error profiling and base correction automatically. Experimental results show that AfterQC can help to eliminate the sequencing errors for pair-end sequencing data to provide much cleaner outputs, and consequently help to reduce the false-positive variants, especially for the low-frequency somatic mutations. While providing rich configurable options, AfterQC can detect and set all the options automatically and require no argument in most cases.

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In recent years, gene fusion detection for cancer treatment has become increasingly important since more therapeutic agents have been developed to suppress fusion kinases. Although a number of tools have been developed to detect gene fusions from DNA sequencing data, most of them are not sensitive enough for processing the data from the samples with low tumor DNA composition, like cell-free tumor DNA. In this paper, we will introduce GeneFuse, a tool to detect and visualize gene fusions with high sensitivity and specificity. GeneFuse focuses on the curated gene fusions, which are available in COSMIC (the Catalogue of Somatic Mutations in Cancer) database. For each detected fusion, GeneFuse reports its genome locus, inferred protein forms, and supporting sequencing reads. The fusion detection results are visualized in an HTML page for cloud-friendly validation. GeneFuse is an open source tool available at GitHub: https://github.com/OpenGene/GeneFuse

The integrin β6 (ITGB6) gene, which encodes the limiting subunit of the integrin αvβ6 heterodimer, plays an important role in wound healing and carcinogenesis. The mechanism underlying ITGB6 regulation, including the identification of DNA elements and cognate transcription factors responsible for basic transcription of human ITGB6 gene, remains unknown. This report describes the cloning and characterization of the human ITGB6 promoter. Using 5′-RACE (rapid amplification of cDNA ends) analysis, the transcriptional initiation site was identified. Promoter deletion analysis identified and functionally validated a TATA box located in the region −24 to −18 base pairs upstream of the ITGB6 promoter. The regulatory elements for transcription of the ITGB6 gene were predominantly located −289 to −150 from the ITGB6 promoter and contained putative binding sites for transcription factors such as STAT3 and C/EBPα. Using chromatin immunoprecipitation assays, this study has demonstrated, for the first time, that transcription factors STAT3 and C/EBPα are involved in the positive regulation of ITGB6 transcription in oral squamous cell carcinoma cells. These findings have important implications for unraveling the mechanism of abnormal ITGB6 activation in tissue remodeling and tumorigenesis.