Review: revisiting the human cholinergic nucleus of the diagonal band of Broca

Although the nucleus of the vertical limb of the diagonal band of Broca (nvlDBB) is the second largest cholinergic nucleus in the basal forebrain, after the nucleus basalis of Meynert (nbM), it has not generally been a focus for studies of neurodegenerative disorders. However, the nvlDBB does have an important projection to the hippocampus and discrete lesions of the rostral basal forebrain have been shown to disrupt retrieval memory function, a major deficit seen in patients with Lewy body disorders. One reason for its neglect is that the anatomical boundaries of the nvlDBB are ill defined and this area of the brain is not part of routine diagnostic sampling protocols. We have reviewed the history and anatomy of the nvlDBB and now propose guidelines for distinguishing nvlDBB from other neighbouring cholinergic cell groups for standardising future clinicopathological work. Thorough review of the literature regarding neurodegenerative conditions reveals inconsistent results in terms of cholinergic neuronal loss within the nvlDBB. This is likely to be due to the use of variable neuronal inclusion criteria and omission of cholinergic immunohistochemical markers. Extrapolating from those studies showing significant nvlDBB neuronal loss in Lewy body dementia, we propose an anatomical and functional connection between the cholinergic component of the nvlDBB (Ch2) and the CA2 subfield in the hippocampus which may be especially vulnerable in Lewy body disorders. This article is protected by copyright. All rights reserved

An intuitive Python interface for Bioconductor libraries demonstrates the utility of language translators

<p>Abstract</p> <p>Background</p> <p>Computer languages can be domain-related, and in the case of multidisciplinary projects, knowledge of several languages will be needed in order to quickly implements ideas. Moreover, each computer language has relative strong points, making some languages better suited than others for a given task to be implemented. The Bioconductor project, based on the R language, has become a reference for the numerical processing and statistical analysis of data coming from high-throughput biological assays, providing a rich selection of methods and algorithms to the research community. At the same time, Python has matured as a rich and reliable language for the agile development of prototypes or final implementations, as well as for handling large data sets.</p> <p>Results</p> <p>The data structures and functions from Bioconductor can be exposed to Python as a regular library. This allows a fully transparent and native use of Bioconductor from Python, without one having to know the R language and with only a small community of <it>translators</it> required to know both. To demonstrate this, we have implemented such Python representations for key infrastructure packages in Bioconductor, letting a Python programmer handle annotation data, microarray data, and next-generation sequencing data.</p> <p>Conclusions</p> <p>Bioconductor is now not solely reserved to R users. Building a Python application using Bioconductor functionality can be done just like if Bioconductor was a Python package. Moreover, similar principles can be applied to other languages and libraries. Our Python package is available at: <url>http://pypi.python.org/pypi/rpy2-bioconductor-extensions/</url></p

Redundancy in Genotyping Arrays

Despite their unprecedented density, current SNP genotyping arrays contain large amounts of redundancy, with up to 40 oligonucleotide features used to query each SNP. By using publicly available reference genotype data from the International HapMap, we show that 93.6% sensitivity at <5% false positive rate can be obtained with only four probes per SNP, compared with 98.3% with the full data set. Removal of this redundancy will allow for more comprehensive whole-genome association studies with increased SNP density and larger sample sizes

Assessment of B Cell Repertoire in Humans

The B cell receptor (BCR) repertoire is highly diverse. Repertoire diversity is achieved centrally by somatic recombination of immunoglobulin (Ig) genes and peripherally by somatic hypermutation and Ig heavy chain class-switching. Throughout these processes, there is selection for functional gene rearrangements, selection against gene combinations resulting in self-reactive BCRs, and selection for BCRs with high affinity for exogenous antigens after challenge. Hence, investigation of BCR repertoires from different groups of B cells can provide information on stages of B cell development and shed light on the etiology of B cell pathologies. In most instances, the third complementarity determining region of the Ig heavy chain (CDR-H3) contributes the majority of amino acids to the antibody/antigen binding interface. Although CDR-H3 spectratype analysis provides information on the overall diversity of BCR repertoires, this fairly simple technique analyzes the relative quantities of CDR-H3 regions of each size, within a range of approximately 10–80 bp, without sequence detail and thus is limited in scope. High-throughput sequencing (HTS) techniques on the Roche 454 GS FLX Titanium system, however, can generate a wide coverage of Ig sequences to provide more qualitative data such as V, D, and J usage as well as detailed CDR3 sequence information. Here we present protocols in detail for CDR-H3 spectratype analysis and HTS of human BCR repertoires

TiArA: A Virtual Appliance for the Analysis of Tiling Array Data

Genomic tiling arrays have been described in the scientific literature since 2003, yet there is a shortage of user-friendly applications available for their analysis.Tiling Array Analyzer (TiArA) is a software program that provides a user-friendly graphical interface for the background subtraction, normalization, and summarization of data acquired through the Affymetrix tiling array platform. The background signal is empirically measured using a group of nonspecific probes with varying levels of GC content and normalization is performed to enforce a common dynamic range.TiArA is implemented as a standalone program for Linux systems and is available as a cross-platform virtual machine that will run under most modern operating systems using virtualization software such as Sun VirtualBox or VMware. The software is available as a Debian package or a virtual appliance at http://purl.org/NET/tiara

Analyzing ChIP-chip Data Using Bioconductor

Analyzing ChIP-chip Data Using Bioconducto

Comprehensive Analysis of Affymetrix Exon Arrays Using BioConductor

ISSN:1553-734XISSN:1553-735

Differential expression analysis for sequence count data

*Motivation:* High-throughput nucleotide sequencing provides quantitative readouts in assays for RNA expression (RNA-Seq), protein-DNA binding (ChIP-Seq) or cell counting (barcode sequencing). Statistical inference of differential signal in such data requires estimation of their variability throughout the dynamic range. When the number of replicates is small, error modelling is needed to achieve statistical power.&#xd;&#xa;&#xd;&#xa;*Results:* We propose an error model that uses the negative binomial distribution, with variance and mean linked by local regression, to model the null distribution of the count data. The method controls type-I error and provides good detection power. &#xd;&#xa;&#xd;&#xa;*Availability:* A free open-source R software package, _DESeq_, is available from the Bioconductor project and from &#x22;http://www-huber.embl.de/users/anders/DESeq&#x22;:http://www-huber.embl.de/users/anders/DESeq