Number of reads that are mapped to chromosome 2 of Arabidopsis.

<p>Number of reads that are mapped to chromosome 2 of Arabidopsis.</p

Performance comparison of RNA-CODE vs Metaxa. Both tools were applied using the default parameters.

<p>Performance comparison of RNA-CODE vs Metaxa. Both tools were applied using the default parameters.</p

Performance of RNA-CODE (multiple-k), SSAKE, and RNA-CODE (single-k) on transcribed ncRNA families.

<p>Performance of RNA-CODE (multiple-k), SSAKE, and RNA-CODE (single-k) on transcribed ncRNA families.</p

The pipeline of RNA-CODE.

<p>The pipeline of RNA-CODE. For miRNAs, the output of the first stage (SCFG-based filtration) and the whole pipeline will be used together for reads classification.</p

Three types of contigs.

<p>Three types of contigs.</p

Additional file 1 of High-resolution strain-level microbiome composition analysis from short reads

Additional file 1. Supplementary sections, figures, and tables. Supplementary information is contained in the additional PDF file

Identifying TF-MiRNA Regulatory Relationships Using Multiple Features

<div><p>MicroRNAs are known to play important roles in the transcriptional and post-transcriptional regulation of gene expression. While intensive research has been conducted to identify miRNAs and their target genes in various genomes, there is only limited knowledge about how microRNAs are regulated. In this study, we construct a pipeline that can infer the regulatory relationships between transcription factors and microRNAs from ChIP-Seq data with high confidence. In particular, after identifying candidate peaks from ChIP-Seq data, we formulate the inference as a PU learning (learning from only positive and unlabeled examples) problem. Multiple features including the statistical significance of the peaks, the location of the peaks, the transcription factor binding site motifs, and the evolutionary conservation are derived from peaks for training and prediction. To further improve the accuracy of our inference, we also apply a mean reciprocal rank (MRR)-based method to the candidate peaks. We apply our pipeline to infer TF-miRNA regulatory relationships in mouse embryonic stem cells. The experimental results show that our approach provides very specific findings of TF-miRNA regulatory relationships.</p></div

The recall and removal rate of prediction on Esrrb-miRNA relationships using protein-coding gene related positive data sets of transcription factor Esrrb and five-fold cross validation.

<p>In each panel, the x-axis denotes the parameter <i>p</i> of SVMlight, it ranges from 0.05 to 0.95 with a step size of 0.05. The y-axis denotes the recall (left panel) and the removal rate (right panel) of the prediction, respectively.</p

The recall and removal rate of prediction on Oct4-miRNA relationships using only known regulation between Oct4 and miRNA genes.

<p>In each panel, the x-axis denotes the parameter <i>p</i> of SVMlight. The y-axis denotes the recall (left panel) and the removal rate (right panel) of the prediction, respectively. f10 means 10% of the known positive examples are put into the unlabeled data,and f30, ⋯, f90 mean 30%, ⋯, 90% of the know positive examples are put into the unlabeled data, respectively.</p

Performance comparison between different assembly tools in assembling genes from butyrate kinase family on the synthetic metagenomic data set.

<p>The memory usage for all tools is based on a single overlap threshold or <i>k-mer</i> and is evaluated as the peak memory usage of the tools. The running time was the average running time on all input families.</p