SUMMARY: HLA allele imputation from SNP genotypes has become increasingly useful, but its accuracy is heavily dependent on the reference panels used. HLA-IMPUTER implements HIBAG algorithm for HLA imputation with different population specific reference panels, including a new Han Chinese reference panel derived from 10 689 samples. We provide a convenient platform for researchers to impute HLA alleles and perform association analysis. AVAILABILITY AND IMPLEMENTATION: http://wyanglab.org: 3838/RefPanelWebsite/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Rapid development in sequencing technologies has dramatically improved our ability to detect genetic variants in human genome. However, current methods have variable sensitivities in detecting different types of genetic variants. One type of such genetic variants that is especially hard to detect is inversions. Analysis of public databases showed that few short inversions have been reported so far. Unlike reads that contain small insertions or deletions, which will be considered through gap alignment, reads carrying short inversions often have poor mapping quality or are unmapped, thus are often not further considered. As a result, the majority of short inversions might have been overlooked and require special algorithms for their detection. RESULTS: Here, we introduce SRinversion, a framework to analyze poorly mapped or unmapped reads by splitting and re-aligning them for the purpose of inversion detection. SRinversion is very sensitive to small inversions and can detect those less than 10 bp in size. We applied SRinversion to both simulated data and high-coverage sequencing data from the 1000 Genomes Project and compared the results with those from Pindel, BreakDancer, DELLY, Gustaf and MID. A better performance of SRinversion was achieved for both datasets for the detection of small inversions. AVAILABILITY AND IMPLEMENTATION: SRinversion is implemented in Perl and is publicly available at http://paed.hku.hk/genome/software/SRinversion/index.html CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

UNLABELLED: Next-generation sequencing has become a valuable tool for detecting mutations involved in Mendelian diseases. However, it is a challenge to identify the small subset of functionally important mutations from tens of thousands of rare variants in a whole exome/genome. Therefore, we developed a toolkit called PriVar, a systematic prioritization pipeline that takes into consideration calling quality of the variants, their predicted functional impact, known connection of the gene to the disease and the number of mutations in a gene, and inference from linkage analysis. AVAILABILITY: Executable jar package is available at http://paed.hku.hk/uploadarea/yangwl/html/software.html.

MOTIVATION: With the knowledge of large number of SNPs in human genome and the fast development in high-throughput genotyping technologies, identification of linked regions in linkage analysis through allele sharing status determination will play an ever important role, while consideration of recombination fractions becomes unnecessary. RESULTS: In this study, we have developed a rule-based program that identifies linked regions for underlined diseases using allele sharing information among family members. Our program uses high-density SNP genotype data and works in the face of genotyping errors. It works on nuclear family structures with two or more siblings. The program graphically displays allele sharing status for all members in a pedigree and identifies regions that are potentially linked to the underlined diseases according to user-specified inheritance mode and penetrance. Extensive simulations based on the chi(2) model for recombination show that our program identifies linked regions with high sensitivity and accuracy. Graphical display of allele sharing status helps to detect misspecification of inheritance mode and penetrance, as well as mislabeling or misdiagnosis. Allele sharing determination may represent the future direction of linkage analysis due to its better adaptation to high-density SNP genotyping data. AVAILABILITY: http://paed.hku.hk/uploadarea/yangwl/html/index.html