BACKGROUND: Short-read aligners have recently gained a lot of speed by exploiting the massive parallelism of GPU. An uprising alterative to GPU is Intel MIC; supercomputers like Tianhe-2, currently top of TOP500, is built with 48,000 MIC boards to offer ~55 PFLOPS. The CPU-like architecture of MIC allows CPU-based software to be parallelized easily; however, the performance is often inferior to GPU counterparts as an MIC card contains only ~60 cores (while a GPU card typically has over a thousand cores). RESULTS: To better utilize MIC-enabled computers for NGS data analysis, we developed a new short-read aligner MICA that is optimized in view of MIC's limitation and the extra parallelism inside each MIC core. By utilizing the 512-bit vector units in the MIC and implementing a new seeding strategy, experiments on aligning 150 bp paired-end reads show that MICA using one MIC card is 4.9 times faster than BWA-MEM (using 6 cores of a top-end CPU), and slightly faster than SOAP3-dp (using a GPU). Furthermore, MICA's simplicity allows very efficient scale-up when multiple MIC cards are used in a node (3 cards give a 14.1-fold speedup over BWA-MEM). SUMMARY: MICA can be readily used by MIC-enabled supercomputers for production purpose. We have tested MICA on Tianhe-2 with 90 WGS samples (17.47 Tera-bases), which can be aligned in an hour using 400 nodes. MICA has impressive performance even though MIC is only in its initial stage of development. AVAILABILITY AND IMPLEMENTATION: MICA's source code is freely available at http://sourceforge.net/projects/mica-aligner under GPL v3. SUPPLEMENTARY INFORMATION: Supplementary information is available as "Additional File 1". Datasets are available at www.bio8.cs.hku.hk/dataset/mica.

MOTIVATION: Transcriptome sequencing has long been the favored method for quickly and inexpensively obtaining a large number of gene sequences from an organism with no reference genome. Owing to the rapid increase in throughputs and decrease in costs of next- generation sequencing, RNA-Seq in particular has become the method of choice. However, the very short reads (e.g. 2 × 90 bp paired ends) from next generation sequencing makes de novo assembly to recover complete or full-length transcript sequences an algorithmic challenge. RESULTS: Here, we present SOAPdenovo-Trans, a de novo transcriptome assembler designed specifically for RNA-Seq. We evaluated its performance on transcriptome datasets from rice and mouse. Using as our benchmarks the known transcripts from these well-annotated genomes (sequenced a decade ago), we assessed how SOAPdenovo-Trans and two other popular transcriptome assemblers handled such practical issues as alternative splicing and variable expression levels. Our conclusion is that SOAPdenovo-Trans provides higher contiguity, lower redundancy and faster execution. AVAILABILITY AND IMPLEMENTATION: Source code and user manual are available at http://sourceforge.net/projects/soapdenovotrans/.