MOTIVATION: A variety of in silico tools have been developed and frequently used to aid high-throughput rapid variant classification, but their performances vary, and their ability to classify variants of uncertain significance were not systemically assessed previously due to lack of validation data. This has been changed recently by advances of functional assays, where functional impact of genetic changes can be measured in single-nucleotide resolution using saturation genome editing (SGE) assay. RESULTS: We demonstrated the neural network model AIVAR (Artificial Intelligent VARiant classifier) was highly comparable to human experts on multiple verified datasets. Although highly accurate on known variants, AIVAR together with CADD and PhyloP showed non-significant concordance with SGE function scores. Moreover, our results indicated that neural network model trained from functional assay data may not produce accurate prediction on known variants. AVAILABILITY AND IMPLEMENTATION: All source code of AIVAR is deposited and freely available at https://github.com/TopGene/AIvar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The next-generation high-throughput sequencing technologies, especially from Illumina, have been widely used in re-sequencing and de novo assembly studies. However, there is no existing software that can simulate Illumina reads with real error and quality distributions and coverage bias yet, which is very useful in relevant software development and study designing of sequencing projects. RESULTS: We provide a software package, pIRS (profile-based Illumina pair-end reads simulator), which simulates Illumina reads with empirical Base-Calling and GC%-depth profiles trained from real re-sequencing data. The error and quality distributions as well as coverage bias patterns of simulated reads using pIRS fit the properties of real sequencing data better than existing simulators. In addition, pIRS also comes with a tool to simulate the heterozygous diploid genomes. AVAILABILITY: pIRS is written in C++ and Perl, and is freely available at ftp://ftp.genomics.org.cn/pub/pIRS/.

MOTIVATION: The boost of next-generation sequencing technologies provides us with an unprecedented opportunity for elucidating genetic mysteries, yet the short-read length hinders us from better assembling the genome from scratch. New protocols now exist that can generate overlapping pair-end reads. By joining the 3' ends of each read pair, one is able to construct longer reads for assembling. However, effectively joining two overlapped pair-end reads remains a challenging task. RESULT: In this article, we present an efficient tool called Connecting Overlapped Pair-End (COPE) reads, to connect overlapping pair-end reads using k-mer frequencies. We evaluated our tool on 30× simulated pair-end reads from Arabidopsis thaliana with 1% base error. COPE connected over 99% of reads with 98.8% accuracy, which is, respectively, 10 and 2% higher than the recently published tool FLASH. When COPE is applied to real reads for genome assembly, the resulting contigs are found to have fewer errors and give a 14-fold improvement in the N50 measurement when compared with the contigs produced using unconnected reads. AVAILABILITY AND IMPLEMENTATION: COPE is implemented in C++ and is freely available as open-source code at ftp://ftp.genomics.org.cn/pub/cope. CONTACT: [email protected] or [email protected]