MOTIVATION: Reliable identification of expressed somatic insertions/deletions (indels) is an unmet need due to artifacts generated in PCR-based RNA-Seq library preparation and the lack of normal RNA-Seq data, presenting analytical challenges for discovery of somatic indels in tumor transcriptome. RESULTS: We present RNAIndel, a tool for predicting somatic, germline and artifact indels from tumor RNA-Seq data. RNAIndel leverages features derived from indel sequence context and biological effect in a machine-learning framework. Except for tumor samples with microsatellite instability, RNAIndel robustly predicts 88-100% of somatic indels in five diverse test datasets of pediatric and adult cancers, even recovering subclonal (VAF range 0.01-0.15) driver indels missed by targeted deep-sequencing, outperforming the current best-practice for RNA-Seq variant calling which had 57% sensitivity but with 14 times more false positives. AVAILABILITY AND IMPLEMENTATION: RNAIndel is freely available at https://github.com/stjude/RNAIndel. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics (2019) doi: 10.1093/bioinformatics/btz753 In the above paper, Table 4 has been corrected as follows. Original: Corrected version:

FindSounds Palette integrates audio recording, editing, and retrieval with advances in audio visualization and content-based queries of local and remote sound collections. Sound designers, musicians, filmmakers, and game developers require software tools for recording and editing audio, and for searching collections of audio files, including sound effects and musical instrument samples

Many current optical character recognition (OCR) systems attempt to decompose printed pages into a set of zones, each containing a single column of text, before converting the characters into coded form. The authors present a methodology for automatically assessing the accuracy of such decompositions, and demonstrate its use in evaluating six OCR systems.>

When optical character recognition (OCR) devices process the same page image, they generate similar text strings. Differences are due to recognition errors. A page of text rarely contains long repeated substrings; therefore, N strings generated by OCR devices can be quickly matched by detecting long common substrings. An algorithm for matching an arbitrary number of strings based on this principle is presented. Although its worst-case performance is O(Nn2), its performance in practice has been observed to be O(Nn log n), where n is the length of a string. This algorithm has been successfully used to study OCR errors, to determine the accuracy of OCR devices, and to implement a voting algorithm.

Requirements for the objective evaluation of automated data-entry systems are presented. Because the cost of correcting errors dominates the document conversion process, the most important characteristic of an OCR device is accuracy. However, different measures of accuracy (error metrics) are appropriate for different applications, and at the character, word, text-line, text-block, and document levels. For wholly objective assessment, OCR devices must be tested under programmed, rather than interactive, control.>