WoLF PSORT is an extension of the PSORT II program for protein subcellular location prediction. WoLF PSORT converts protein amino acid sequences into numerical localization features; based on sorting signals, amino acid composition and functional motifs such as DNA-binding motifs. After conversion, a simple k-nearest neighbor classifier is used for prediction. Using html, the evidence for each prediction is shown in two ways: (i) a list of proteins of known localization with the most similar localization features to the query, and (ii) tables with detailed information about individual localization features. For convenience, sequence alignments of the query to similar proteins and links to UniProt and Gene Ontology are provided. Taken together, this information allows a user to understand the evidence (or lack thereof) behind the predictions made for particular proteins. WoLF PSORT is available at wolfpsort.org

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The main way of analyzing biological sequences is by comparing and aligning them to each other. It remains difficult, however, to compare modern multi-billionbase DNA data sets. The difficulty is caused by the nonuniform (oligo)nucleotide composition of these sequences, rather than their size per se. To solve this problem, we modified the standard seed-and-extend approach (e.g., BLAST) to use adaptive seeds. Adaptive seeds are matches that are chosen based on their rareness, instead of using fixed-length matches. This method guarantees that the number of matches, and thus the running time, increases linearly, instead of quadratically, with sequence length. LAST, our open source implementation of adaptive seeds, enables fast and sensitive comparison of large sequences with arbitrarily nonuniform composition.[Supplemental material is available for this article. LAST software is freely available at http://last.cbrc.jp.]Biomedical research is being revolutionized by multi-gigabase DNA data sets. This began with the sequencing of whole large genomes, such as the human (;3 billion bases), allowing us to see our species' genetic blueprint. More recently, new sequencing technologies have enabled small-scale laboratories to produce gigabases of DNA sequence. These technologies have been used to explore DNA from environmental samples, transcribed RNA in tissues and cell lines, chromatin structure, and personal genomes, to name just a few applications (Metzker 2010).In all cases, the data largely remain an uninterpretable sea of As, Cs, Gs, and Ts, unless we make connections by comparing the sequences to each other. For example, we can predict the taxonomy and function of environmental DNA reads by comparing them to all known protein sequences (via the genetic code). We can interpret DNA reads from an extinct organism (e.g., the saber tooth tiger) by mapping them to the genome of a surviving organism (e.g., the cat). In all cases, the initial task is to find similar regions between huge sequence data sets.The classic tool for this task is BLAST (and similar methods such as PatternHunter, BLAT, BLASTZ, YASS, and many others) (Altschul et al. 1997;Kent 2002;Ma et al. 2002;Schwartz et al. 2003;Kucherov et al. 2006). These methods rely on a seed-andextend heuristic. They rapidly find similarities between the ''query'' sequence and the ''target'' sequence by using short matches called seeds. These seeds act as starting points for the subsequent timeconsuming alignment extensions. The simplest kind of seed consists of exact matches of a fixed-length (e.g., 12 bases). Short seed lengths can improve sensitivity, but at a high cost in running time, because they yield more seed matches and thus more extensions. On the other hand, long seeds are matched rarely and lead to decreased sensitivity.In this work, we propose adaptive seeds as an alternative to fixed-length seeds. As implied by the name, fixed-length seeds have a constant length l. In contrast, adaptive seeds vary in length-seeds are lengthened until the number of matches in...