GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies

Background  

Microarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in the gene sets.     

MASPECTRAS: a platform for management and analysis of proteomics LC-MS/MS data

Background  

The advancements of proteomics technologies have led to a rapid increase in the number, size and rate at which datasets are generated. Managing and extracting valuable information from such datasets requires the use of data management platforms and computational approaches.     

Discover protein sequence signatures from protein-protein interaction data

Background  

The development of high-throughput technologies such as yeast two-hybrid systems and mass spectrometry technologies has made it possible to generate large protein-protein interaction (PPI) datasets. Mining these datasets for underlying biological knowledge has, however, remained a challenge.     

Expression profiling of human renal carcinomas with functional taxonomic analysis

Background  

Molecular characterization has contributed to the understanding of the inception, progression, treatment and prognosis of cancer. Nucleic acid array-based technologies extend molecular characterization of tumors to thousands of gene products. To effectively discriminate between tumor sub-types, reliable laboratory techniques and analytic methods are required.     

A database and API for variation,dense genotyping and resequencing data

Background  

Advances in sequencing and genotyping technologies are leading to the widespread availability of multi-species variation data, dense genotype data and large-scale resequencing projects. The 1000 Genomes Project and similar efforts in other species are challenging the methods previously used for storage and manipulation of such data necessitating the redesign of existing genome-wide bioinformatics resources.     

Discover Protein Complexes in Protein-Protein Interaction Networks Using Parametric Local Modularity

Background  

Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in multiple species and in both normal and diseased cells. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively distil network models from large-scale interactome data.     

Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumours and matched controls

Background  

Ultra-high throughput sequencing technologies provide opportunities both for discovery of novel molecular species and for detailed comparisons of gene expression patterns. Small RNA populations are particularly well suited to this analysis, as many different small RNAs can be completely sequenced in a single instrument run.     

PCR-based gene synthesis to produce recombinant proteins for crystallization

Background  

Gene synthesis technologies are an important tool for structural biology projects, allowing increased protein expression through codon optimization and facilitating sequence alterations. Existing methods, however, can be complex and not always reproducible, prompting researchers to use commercial suppliers rather than synthesize genes themselves.     

MochiView: versatile software for genome browsing and DNA motif analysis

Background  

As high-throughput technologies rapidly generate genome-scale data, it becomes increasingly important to visually integrate these data so that specific hypotheses can be formulated and tested.     

Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data

Background  

Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer.     

High-throughput sequence alignment using Graphics Processing Units

Background  

The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and de novo genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for analysis of these data, but researchers will need ever faster, high-throughput alignment tools running on inexpensive hardware to keep up with new sequence technologies.     

The High Throughput Sequence Annotation Service (HT-SAS) – the shortcut from sequence to true Medline words

Background  

Advances in high-throughput technologies available to modern biology have created an increasing flood of experimentally determined facts. Ordering, managing and describing these raw results is the first step which allows facts to become knowledge. Currently there are limited ways to automatically annotate such data, especially utilizing information deposited in published literature.     

Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis

Background  

Lipids are an important and highly diverse class of molecules having structural, energy storage and signaling roles. Modern analytical technologies afford screening of many lipid molecular species in parallel. One of the biggest challenges of lipidomics is elucidation of important pathobiological phenomena from the integration of the large amounts of new data becoming available.     

DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI

Background  

Next-generation sequencing technologies have led to the high-throughput production of sequence data (reads) at low cost. However, these reads are significantly shorter and more error-prone than conventional Sanger shotgun reads. This poses a challenge for the de novo assembly in terms of assembly quality and scalability for large-scale short read datasets.     

Data structures and compression algorithms for high-throughput sequencing technologies

Background  

High-throughput sequencing (HTS) technologies play important roles in the life sciences by allowing the rapid parallel sequencing of very large numbers of relatively short nucleotide sequences, in applications ranging from genome sequencing and resequencing to digital microarrays and ChIP-Seq experiments. As experiments scale up, HTS technologies create new bioinformatics challenges for the storage and sharing of HTS data.     

An improved method for rapid generation of unmarked <Emphasis Type="Italic">Pseudomonas aeruginosa</Emphasis> deletion mutants

Background  

Traditional gene replacement procedures are still time-consuming. They usually necessitate cloning of the gene to be mutated, insertional inactivation of the gene with an antibiotic resistance cassette and exchange of the plasmid-borne mutant allele with the bacterial chromosome. PCR and recombinational technologies can be exploited to substantially accelerate virtually all steps involved in the gene replacement process.     

PanGEA: Identification of allele specific gene expression using the 454 technology

Background  

Next generation sequencing technologies hold great potential for many biological questions. While mainly used for genomic sequencing, they are also very promising for gene expression profiling. Sequencing of cDNA does not only provide an estimate of the absolute expression level, it can also be used for the identification of allele specific gene expression.     

Adaptable data management for systems biology investigations

Background  

Within research each experiment is different, the focus changes and the data is generated from a continually evolving barrage of technologies. There is a continual introduction of new techniques whose usage ranges from in-house protocols through to high-throughput instrumentation. To support these requirements data management systems are needed that can be rapidly built and readily adapted for new usage.