SUMMARY: With the development of new high-throughput DNA sequencing technologies and decreasing costs, large gene expression datasets are being generated at an accelerating rate, but can be complex to visualize. New, more interactive and intuitive tools are needed to visualize the spatiotemporal context of expression data and help elucidate gene function. Using tomato fruit as a model, we have developed the Tomato Expression Atlas to facilitate effective data analysis, allowing the simultaneous visualization of groups of genes at a cell/tissue level of resolution within an organ, enhancing hypothesis development and testing in addition to candidate gene identification. This atlas can be adapted to different types of expression data from diverse multicellular species. AVAILABILITY AND IMPLEMENTATION: The Tomato Expression Atlas is available at http://tea.solgenomics.net/ . Source code is available at https://github.com/solgenomics/Tea . CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The usability of the NGS technologies heavily relies on the accuracy of the data. Many research groups developed different programs to clean the sequenced raw data by removing adapter contamination and trimming low-quality nucleotides. However, they are not optimized to process data from any specific equipment. In this study, we present an optimized pipeline Fastq_clean to clean the DNA-seq and RNA-seq data from the illumina sequencer. Fastq_clean can remove the low quality nucleotides and adapter contamination precisely and keep as many of the qualified nucleotides as possible. Fastq_clean can batchly process sequenced data and export statistics information for the data quality control (QC) by running a single command line. Compared with two most used tools on a published dataset, Fastq_clean reached the best performance. Fastq_clean has already been successfully used in some genome or transcriptome projects and it can also be used to clean the NGS data from other sequencers (e.g. 454), but needs some modification to reach the rest performance.

BACKGROUND: Expressed Sequence Tags (ESTs) have played significant roles in gene discovery and gene functional analysis, especially for non-model organisms. For organisms with no full genome sequences available, ESTs are normally assembled into longer consensus sequences for further downstream analysis. However current de novo EST assembly programs often generate large number of assembly errors that will negatively affect the downstream analysis. In order to generate more accurate consensus sequences from ESTs, tools are needed to reduce or eliminate errors from de novo assemblies. RESULTS: We present iAssembler, a pipeline that can assemble large-scale ESTs into consensus sequences with significantly higher accuracy than current existing assemblers. iAssembler employs MIRA and CAP3 assemblers to generate initial assemblies, followed by identifying and correcting two common types of transcriptome assembly errors: 1) ESTs from different transcripts (mainly alternatively spliced transcripts or paralogs) are incorrectly assembled into same contigs; and 2) ESTs from same transcripts fail to be assembled together. iAssembler can be used to assemble ESTs generated using the traditional Sanger method and/or the Roche-454 massive parallel pyrosequencing technology. CONCLUSION: We compared performances of iAssembler and several other de novo EST assembly programs using both Roche-454 and Sanger EST datasets. It demonstrated that iAssembler generated significantly more accurate consensus sequences than other assembly programs.

MOTIVATION: MicroRNAs miRNAs play important roles in gene regulation and are regarded as key components in gene regulatory pathways. Systematically understanding functional roles of miRNAs is essential to define core transcriptional units regulating key biological processes. Here, we propose a method based on the probabilistic graphical model to identify the regulatory modules of miRNAs and the core regulatory motifs involved in their ability to regulate gene expression. RESULTS: We applied our method to datasets of different sources from Arabidopsis consisting of miRNA-target pair information, upstream sequences of miRNAs, transcriptional regulatory motifs and gene expression profiles. The graphical model used in this study can efficiently capture the relationship between miRNAs and diverse conditions such as various developmental processes, thus allowing us to detect functionally correlated miRNA regulatory modules involved in specific biological processes. Furthermore, this approach can reveal core transcriptional elements associated with their miRNAs. The proposed method found clusters of miRNAs, as well as putative regulators controlling the expression of miRNAs, which were highly related to diverse developmental processes of Arabidopsis. Consequently, our method can provide hypothetical miRNA regulatory circuits for functional testing that represent transcriptional events of miRNAs and transcriptional factors involved in gene regulatory pathways.

BACKGROUND: MicroRNAs (miRNAs) are small and noncoding RNAs that play important roles in various biological processes. They regulate target mRNAs post-transcriptionally through complementary base pairing. Since the changes of miRNAs affect the expression of target genes, the expression levels of target genes in specific biological processes could be different from those of non-target genes. Here we demonstrate that gene expression profiles contain useful information in separating miRNA targets from non-targets. RESULTS: The gene expression profiles related to various developmental processes and stresses, as well as the sequences of miRNAs and mRNAs in Arabidopsis, were used to determine whether a given gene is a miRNA target. It is based on the model combining the support vector machine (SVM) classifier and the scoring method based on complementary base pairing between miRNAs and mRNAs. The proposed model yielded low false positive rate and retrieved condition-specific candidate targets through a genome-wide screening. CONCLUSION: Our approach provides a novel framework into screening target genes by considering the gene regulation of miRNAs. It can be broadly applied to identify condition-specific targets computationally by embedding information of gene expression profiles.