Because the new Proton platform from Life Technologies produced markedly different data from those of the Illumina platform, the conventional Illumina data analysis pipeline could not be used directly. We developed an optimized SNP calling method using TMAP and GATK (OTG-snpcaller). This method combined our own optimized processes, Remove Duplicates According to AS Tag (RDAST) and Alignment Optimize Structure (AOS), together with TMAP and GATK, to call SNPs from Proton data. We sequenced four sets of exomes captured by Agilent SureSelect and NimbleGen SeqCap EZ Kit, using Life Technology’s Ion Proton sequencer. Then we applied OTG-snpcaller and compared our results with the results from Torrent Variants Caller. The results indicated that OTG-snpcaller can reduce both false positive and false negative rates. Moreover, we compared our results with Illumina results generated by GATK best practices, and we found that the results of these two platforms were comparable. The good performance in variant calling using GATK best practices can be primarily attributed to the high quality of the Illumina sequences.

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Summary In this paper, we propose a novel robust principal component analysis (PCA) for high‐dimensional data in the presence of various heterogeneities, in particular strong tailing and outliers. A transformation motivated by the characteristic function is constructed to improve the robustness of the classical PCA. The suggested method has the distinct advantage of dealing with heavy‐tail‐distributed data, whose covariances may be non‐existent (positively infinite, for instance), in addition to the usual outliers. The proposed approach is also a case of kernel principal component analysis (KPCA) and employs the robust and non‐linear properties via a bounded and non‐linear kernel function. The merits of the new method are illustrated by some statistical properties, including the upper bound of the excess error and the behaviour of the large eigenvalues under a spiked covariance model. Additionally, using a variety of simulations, we demonstrate the benefits of our approach over the classical PCA. Finally, using data on protein expression in mice of various genotypes in a biological study, we apply the novel robust PCA to categorise the mice and find that our approach is more effective at identifying abnormal mice than the classical PCA.

Second generation sequencing has been widely used to sequence whole genomes. Though various paired-end sequencing methods have been developed to construct the long scaffold from contigs derived from shotgun sequencing, the classical paired-end sequencing of the Bacteria Artificial Chromosome (BAC) or fosmid libraries by the Sanger method still plays an important role in genome assembly. However, sequencing libraries with the Sanger method is expensive and time-consuming. Here we report a new strategy to sequence the paired-ends of genomic libraries with parallel pyrosequencing, using a Chinese amphioxus (Branchiostoma belcheri) BAC library as an example. In total, approximately 12,670 non-redundant paired-end sequences were generated. Mapping them to the primary scaffolds of Chinese amphioxus, we obtained 413 ultra-scaffolds from 1,182 primary scaffolds, and the N50 scaffold length was increased approximately 55 kb, which is about a 10% improvement. We provide a universal and cost-effective method for sequencing the ultra-long paired-ends of genomic libraries. This method can be very easily implemented in other second generation sequencing platforms.