MOTIVATION: Shotgun sequence read data derived from xenograft material contains a mixture of reads arising from the host and reads arising from the graft. Classifying the read mixture to separate the two allows for more precise analysis to be performed. RESULTS: We present a technique, with an associated tool Xenome, which performs fast, accurate and specific classification of xenograft-derived sequence read data. We have evaluated it on RNA-Seq data from human, mouse and human-in-mouse xenograft datasets. AVAILABILITY: Xenome is available for non-commercial use from http://www.nicta.com.au/bioinformatics.

MOTIVATION: The de novo assembly of short read high-throughput sequencing data poses significant computational challenges. The volume of data is huge; the reads are tiny compared to the underlying sequence, and there are significant numbers of sequencing errors. There are numerous software packages that allow users to assemble short reads, but most are either limited to relatively small genomes (e.g. bacteria) or require large computing infrastructure or employ greedy algorithms and thus often do not yield high-quality results. RESULTS: We have developed Gossamer, an implementation of the de Bruijn approach to assembly that requires close to the theoretical minimum of memory, but still allows efficient processing. Our results show that it is space efficient and produces high-quality assemblies. AVAILABILITY: Gossamer is available for non-commercial use from http://www.genomics.csse.unimelb.edu.au/product-gossamer.php.

Genomic repositories increasingly include individual as well as reference sequences, which tend to share long identical and near-identical strings of nucleotides. However, the sequential processing used by most compression algorithms, and the volumes of data involved, mean that these long-range repetitions are not detected. An order-insensitive, disk-based dictionary construction method can detect this repeated content and use it to compress collections of sequences. We explore a dictionary construction method that improves repeat identification in large DNA data sets. Our adaptation, COMRAD, of an existing disk-based method identifies exact repeated content in collections of sequences with similarities within and across the set of input sequences. COMRAD compresses the data over multiple passes, which is an expensive process, but allows COMRAD to compress large data sets within reasonable time and space. COMRAD allows for random access to individual sequences and subsequences without decompressing the whole data set. COMRAD has no competitor in terms of the size of data sets that it can compress (extending to many hundreds of gigabytes) and, even for smaller data sets, the results are competitive compared to alternatives; as an example, 39 S. cerevisiae genomes compressed to 0.25 bits per base.

SUMMARY: The shorter and vastly more numerous reads produced by second-generation sequencing technologies require new tools that can assemble massive numbers of reads in reasonable time. Existing short-read assembly tools can be classified into two categories: greedy extension-based and graph-based. While the graph-based approaches are generally superior in terms of assembly quality, the computer resources required for building and storing a huge graph are very high. In this article, we present Taipan, an assembly algorithm which can be viewed as a hybrid of these two approaches. Taipan uses greedy extensions for contig construction but at each step realizes enough of the corresponding read graph to make better decisions as to how assembly should continue. We show that this approach can achieve an assembly quality at least as good as the graph-based approaches used in the popular Edena and Velvet assembly tools using a moderate amount of computing resources.

We introduce the unoriented $\Theta$-maximum as a new criterion for describing the shape of a set of planar points. We present efficient algorithms for computing the unoriented $\Theta$-maximum of a set of planar points. We also propose a simple linear expected time algorithm for computing the unoriented $\Theta$-maximum of a set of planar points when $\Theta=\pi/2$.

A standard algorithm based on the Kalman Filter and used for tracking multiple targets in a noisy environment has been applied to analysing the motion of cells in video images. The method, known as Probabilistic Data Association, is computationally efficient, allowing simultaneous monitoring of many cells at video frame rates of 25 Hz. The present study uses the tracking procedure to investigate the motion of human sperm cells. Extensions to allow the tracking of many hundreds of cells at video rates using a massively data parallel computer are explained. The procedure is well suited for real time tracking of other visualised biological events.>

Two systolic codebook generators which are based on finding clusters in a multidimensional space with a suitable dissimilarity measure are presented. They are implemented on two novel VLSI architectures. For segments of speech represented by a sequence of k linear-predictive-coding vectors, the first version finds r large clusters in time O(rk), and the second version finds the maximal r clusters in time O(r log k).>