Neural injury triggers swift responses from glia, including glial migration and phagocytic clearance of damaged neurons. The transcriptional programs governing these complex innate glial immune responses are still unclear. Here, we describe a novel injury assay in adult Drosophila that elicits widespread glial responses in the ventral nerve cord (VNC). We profiled injury-induced changes in VNC gene expression by RNA sequencing (RNA-seq) and found that responsive genes fall into diverse signaling classes. One factor, matrix metalloproteinase-1 (MMP-1), is induced in Drosophila ensheathing glia responding to severed axons. Interestingly, glial induction of MMP-1 requires the highly conserved engulfment receptor Draper, as well as AP-1 and STAT92E. In MMP-1 depleted flies, glia do not properly infiltrate neuropil regions after axotomy and, as a consequence, fail to clear degenerating axonal debris. This work identifies Draper-dependent activation of MMP-1 as a novel cascade required for proper glial clearance of severed axons.
Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a ‘life cycle’ view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain.
Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on ‘omics’ datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.
Background:Previously, we described ROVER, a DNA variant caller which identifies genetic variants from PCR-targeted massively parallel sequencing (MPS) datasets generated by the Hi-Plex protocol. ROVER permits stringent filtering of sequencing chemistry-induced errors by requiring reported variants to appear in both reads of overlapping pairs above certain thresholds of occurrence. ROVER was developed in tandem with Hi-Plex and has been used successfully to screen for genetic mutations in the breast cancer predisposition gene PALB2.
ROVER is applied to MPS data in BAM format and, therefore, relies on sequence reads being mapped to a reference genome. In this paper, we describe an improvement to ROVER, called UNDR ROVER (Unmapped primer-Directed ROVER), which accepts MPS data in FASTQ format, avoiding the need for a computationally expensive mapping stage. It does so by taking advantage of the location-specific nature of PCR-targeted MPS data.
Results: The UNDR ROVER algorithm achieves the same stringent variant calling as its predecessor with a significant runtime performance improvement. In one indicative sequencing experiment, UNDR ROVER (in its fastest mode) required 8-fold less sequential computation time than the ROVER pipeline and 13-fold less sequential computation time than a variant calling pipeline based on the popular GATK tool.
UNDR ROVER is implemented in Python and runs on all popular POSIX-like operating systems (Linux, OS X). It requires as input a tab-delimited format file containing primer sequence information, a FASTA format file containing the reference genome sequence, and paired FASTQ files containing sequence reads. Primer sequences at the 5′ end of reads associate read-pairs with their targeted amplicon and, thus, their expected corresponding coordinates in the reference genome. The primer-intervening sequence of each read is compared against the reference sequence from the same location and variants are identified using the same algorithm as ROVER. Specifically, for a variant to be ‘called’ it must appear at the same location in both of the overlapping reads above user-defined thresholds of minimum number of reads and proportion of reads.
Conclusions: UNDR ROVER provides the same rapid and accurate genetic variant calling as its predecessor with greatly reduced computational costs.
Background: The NCBI Entrez Gene and PubMed databases contain a wealth of high-quality information about genes for many different organisms. The NCBI Entrez online web-search interface is convenient for simple manual search for a small number of genes but impractical for the kinds of outputs seen in typical genomics projects.
Results: We have developed an efficient open source tool implemented in Python called Annokey, which annotates gene lists with the results of a keyword search of the NCBI Entrez Gene database and linked Pubmed article information. The user steers the search by specifying a ranked list of keywords (including multi-word phrases and regular expressions) that are correlated with their topic of interest. Rank information of matched terms allows the user to guide further investigation.
We applied Annokey to the entire human Entrez Gene database using the key-term “DNA repair” and assessed its performance in identifying the 176 members of a published “gold standard” list of genes established to be involved in this pathway. For this test case we observed a sensitivity and specificity of 97% and 96%, respectively.
Conclusions: Annokey facilitates the identification of genes related to an area of interest, a task which can be onerous if performed manually on a large number of genes. Annokey provides a way to capitalize on the high quality information provided by the Entrez Gene database allowing both scalability and compatibility with automated analysis pipelines, thus offering the potential to significantly enhance research productivity.
Haskell is a very safe language, particularly because of its type system. However there will always be programs that do the wrong thing. Programmer fallibility, partial or incorrect specifications and typographic errors are but a few of the reasons that make bugs a fact of life. This paper is about the use and implementation of a debugger, called Buddha, which helps Haskell programmers understand why their programs misbehave. Traditional debugging tools that examine the program execution step-by-step are not suitable for Haskell because of its unorthodox evaluation strategy. Instead, a different approach is taken which abstracts away the evaluation order of the program and focuses on its high-level logical meaning.
This style of debugging is called Declarative Debugging, and it has its roots in the Logic Programming community. At the heart of the debugger is a tree which records information about the evaluation of the program in a manner which is easy to relate to the structure of the source code. It resembles a call graph annotated with the arguments and results of function applications, shown in their most evaluated form. Logical relationships between entities in the source are reflected in the links between nodes in the tree. An error diagnosis algorithm is applied to the tree in a top-down fashion in the search for causes of bugs.