Near-optimal Assembly for Shotgun Sequencing with Noisy Reads

Recent work identified the fundamental limits on the information requirements in terms of read length and coverage depth required for successful de novo genome reconstruction from shotgun sequencing data, based on the idealistic assumption of no errors in the reads (noiseless reads). In this work, we show that even when there is noise in the reads, one can successfully reconstruct with information requirements close to the noiseless fundamental limit. A new assembly algorithm, X-phased Multibridging, is designed based on a probabilistic model of the genome. It is shown through analysis to perform well on the model, and through simulations to perform well on real genomes

Identification of integrin drug targets for 17 solid tumor types.

Integrins are contributors to remodeling of the extracellular matrix and cell migration. Integrins participate in the assembly of the actin cytoskeleton, regulate growth factor signaling pathways, cell proliferation, and control cell motility. In solid tumors, integrins are involved in promoting metastasis to distant sites, and angiogenesis. Integrins are a key target in cancer therapy and imaging. Integrin antagonists have proven successful in halting invasion and migration of tumors. Overexpressed integrins are prime anti-cancer drug targets. To streamline the development of specific integrin cancer therapeutics, we curated data to predict which integrin heterodimers are pausible therapeutic targets against 17 different solid tumors. Computational analysis of The Cancer Genome Atlas (TCGA) gene expression data revealed a set of integrin targets that are differentially expressed in tumors. Filtered by FPKM (Fragments Per Kilobase of transcript per Million mapped reads) expression level, overexpressed subunits were paired into heterodimeric protein targets. By comparing the RNA-seq differential expression results with immunohistochemistry (IHC) data, overexpressed integrin subunits were validated. Biologics and small molecule drug compounds against these identified overexpressed subunits and heterodimeric receptors are potential therapeutics against these cancers. In addition, high-affinity and high-specificity ligands against these integrins can serve as efficient vehicles for delivery of cancer drugs, nanotherapeutics, or imaging probes against cancer

BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly.

BackgroundThe problem of de-novo assembly for metagenomes using only long reads is gaining attention. We study whether post-processing metagenomic assemblies with the original input long reads can result in quality improvement. Previous approaches have focused on pre-processing reads and optimizing assemblers. BIGMAC takes an alternative perspective to focus on the post-processing step.ResultsUsing both the assembled contigs and original long reads as input, BIGMAC first breaks the contigs at potentially mis-assembled locations and subsequently scaffolds contigs. Our experiments on metagenomes assembled from long reads show that BIGMAC can improve assembly quality by reducing the number of mis-assemblies while maintaining or increasing N50 and N75. Moreover, BIGMAC shows the largest N75 to number of mis-assemblies ratio on all tested datasets when compared to other post-processing tools.ConclusionsBIGMAC demonstrates the effectiveness of the post-processing approach in improving the quality of metagenomic assemblies