Genomic islands (GIs) that are associated with microbial adaptations and carry sequence patterns different from that of the host are sporadically distributed among closely related species. This bias can dominate the signal of interest in GI detection. However, variations still exist among the segments of the host, although no uniform standard exists regarding the best methods of discriminating GIs from the rest of the genome in terms of compositional bias. In the present work, we proposed a robust software, MTGIpick, which used regions with pattern bias showing multiscale difference levels to identify GIs from the host. MTGIpick can identify GIs from a single genome without annotated information of genomes or prior knowledge from other data sets. When real biological data were used, MTGIpick demonstrated better performance than existing methods, as well as revealed potential GIs with accurate sizes missed by existing methods because of a uniform standard. Software and supplementary are freely available at http://bioinfo.zstu.edu.cn/MTGI or https://github.com/bioinfo0706/MTGIpick.

BACKGROUND: Tumor cells are considered to have an aberrant cell state, and some evidence indicates different development states appearing in the tumorigenesis. Embryonic development and stem cell differentiation are ordered processes in which the sequence of events over time is highly conserved. The "cancer attractor" concept integrates normal developmental processes and tumorigenesis into a high-dimensional "cell state space", and provides a reasonable explanation of the relationship between these two biological processes from theoretical viewpoint. However, it is hard to describe such relationship by using existed experimental data; moreover, the measurement of different development states is also difficult. RESULTS: Here, by applying a novel time-ordered linear model based on a co-bisector which represents the joint direction of a series of vectors, we described the trajectories of development process by a line and showed different developmental states of tumor cells from developmental timescale perspective in a cell state space. This model was used to transform time-course developmental expression profiles of human ESCs, normal mouse liver, ovary and lung tissue into "cell developmental state lines". Then these cell state lines were applied to observe the developmental states of different tumors and their corresponding normal samples. Mouse liver and ovarian tumors showed different similarity to early development stage. Similarly, human glioma cells and ovarian tumors became developmentally "younger". CONCLUSIONS: The time-ordered linear model captured linear projected development trajectories in a cell state space. Meanwhile it also reflected the change tendency of gene expression over time from the developmental timescale perspective, and our finding indicated different development states during tumorigenesis processes in different tissues.

Abstract Summary: We report on a major new version of the RMAP software for mapping reads from short-read sequencing technology. General improvements to accuracy and space requirements are included, along with novel functionality. Included in the RMAP software package are tools for mapping paired-end reads, mapping using more sophisticated use of quality scores, collecting ambiguous mapping locations and mapping bisulfite-treated reads. Availability: The applications described in this note are available for download at http://www.cmb.usc.edu/people/andrewds/rmap and are distributed as Open Source software under the GPLv3.0. The software has been tested on Linux and OS X platforms. Contact: [email protected]; [email protected] The RMAP algorithm was introduced by (Smith et al., 2008) as one of the earliest available programs for mapping reads from the Illumina second-generation sequencing technology. One important contribution of RMAP was to incorporate the use of quality scores directly into the mapping process: read positions with too low a quality score were not considered while mapping, and that quality score cutoff could be adjusted by the user. Subsequently, numerous mapping algorithm have appeared (Langmead et al., 2009; Li,H. et al., 2008; Li,R. et al., 2008; Lin et al., 2008; Schatz, 2009; Yanovsky et al., 2008), with improvements in both efficiency and breadth of functionality (e.g. ability to map paired-end reads; integrated SNP calling). Investigators requiring solutions to mapping problems now have many options. As new applications of short-read sequencing emerge, many variations on the analysis task of read mapping emerge. Diversity in performance characteristics of existing mapping tools becomes potentially valuable. We report the first major update to RMAP. The basic algorithmic framework in RMAP is still to preprocess reads and scan the genome, but several modifications have been made and much additional functionality has been included. Importantly, RMAP has a memory footprint that depends on the number of reads being mapped. This feature allows RMAP to be used effectively in cluster environments with commodity nodes, because partitioning the reads allows natural parallelizations with linear reduction in memory requirements per processor core used. Included in this release of the RMAP software package is functionality for mapping paired-end reads, making more sophisticated use of quality scores, collecting mapping locations for ambiguously mapping reads and mapping bisulfite-treated reads.

BACKGROUND: Second-generation sequencing has the potential to revolutionize genomics and impact all areas of biomedical science. New technologies will make re-sequencing widely available for such applications as identifying genome variations or interrogating the oligonucleotide content of a large sample (e.g. ChIP-sequencing). The increase in speed, sensitivity and availability of sequencing technology brings demand for advances in computational technology to perform associated analysis tasks. The Solexa/Illumina 1G sequencer can produce tens of millions of reads, ranging in length from approximately 25-50 nt, in a single experiment. Accurately mapping the reads back to a reference genome is a critical task in almost all applications. Two sources of information that are often ignored when mapping reads from the Solexa technology are the 3' ends of longer reads, which contain a much higher frequency of sequencing errors, and the base-call quality scores. RESULTS: To investigate whether these sources of information can be used to improve accuracy when mapping reads, we developed the RMAP tool, which can map reads having a wide range of lengths and allows base-call quality scores to determine which positions in each read are more important when mapping. We applied RMAP to analyze data re-sequenced from two human BAC regions for varying read lengths, and varying criteria for use of quality scores. RMAP is freely available for downloading at http://rulai.cshl.edu/rmap/. CONCLUSION: Our results indicate that significant gains in Solexa read mapping performance can be achieved by considering the information in 3' ends of longer reads, and appropriately using the base-call quality scores. The RMAP tool we have developed will enable researchers to effectively exploit this information in targeted re-sequencing projects.