BackgroundAlignment-free sequence comparison approaches have been garnering increasing interest in various data- and compute-intensive applications such as phylogenetic inference for large-scale sequences. While k-mer based methods are predominantly used in real applications, the average common substring (ACS) approach is emerging as one of the prominent alignment-free approaches. This ACS approach has been further generalized by some recent work, either greedily or exactly, by allowing a bounded number of mismatches in the common substrings.ResultsWe present ALFRED-G, a greedy alignment-free distance estimator for phylogenetic tree reconstruction based on the concept of the generalized ACS approach. In this algorithm, we have investigated a new heuristic to efficiently compute the lengths of common strings with mismatches allowed, and have further applied this heuristic to phylogeny reconstruction. Performance evaluation using real sequence datasets shows that our heuristic is able to reconstruct comparable, or even more accurate, phylogenetic tree topologies than the kmacs heuristic algorithm at highly competitive speed.ConclusionsALFRED-G is an alignment-free heuristic for evolutionary distance estimation between two biological sequences. This algorithm is implemented in C++ and has been incorporated into our open-source ALFRED software package (http://alurulab.cc.gatech.edu/phylo).

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Inverted indexes are the most fundamental and widely used data structures in information retrieval. For each unique word occurring in a document collection, the inverted index stores a list of the documents in which this word occurs. Compression techniques are often applied to further reduce the space requirement of these lists. However, the index has a shortcoming, in that only predefined pattern queries can be supported efficiently. In terms of string documents where word boundaries are undefined, if we have to index all the substrings of a given document, then the storage quickly becomes quadratic in the data size. Also, if we want to apply the same type of indexes for querying phrases or sequence of words, then the inverted index will end up storing redundant information. In this paper, we show the first set of inverted indexes which work naturally for strings as well as phrase searching. The central idea is to exclude document d in the inverted list of a string P if every occurrence of P in d is subsumed by another string of which P is a prefix. With this we show that our space utilization is close to the optimal. Techniques from succinct data structures are deployed to achieve compression while allowing fast access in terms of frequency and document id based retrieval. Compression and speed tradeoffs are evaluated for different variants of the proposed index. For phrase searching, we show that our indexes compare favorably against a typical inverted index deploying position-wise intersections. We also show efficient top-k based retrieval under relevance metrics like frequency and tf-idf.

BackgroundHepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood are difficult to detect and investigate. Molecular analysis has been frequently used in the study of HCV outbreaks and transmission chains; helping identify a cluster of sequences as linked by transmission if their genetic distances are below a previously defined threshold. However, HCV exists as a population of numerous variants in each infected individual and it has been observed that minority variants in the source are often the ones responsible for transmission, a situation that precludes the use of a single sequence per individual because many such transmissions would be missed.The use of Next-Generation Sequencing immensely increases the sensitivity of transmission detection but brings a considerable computational challenge because all sequences need to be compared among all pairs of samples.MethodsWe developed a three-step strategy that filters pairs of samples according to different criteria: (i) a k-mer bloom filter, (ii) a Levenhstein filter and (iii) a filter of identical sequences. We applied these three filters on a set of samples that cover the spectrum of genetic relationships among HCV cases, from being part of the same transmission cluster, to belonging to different subtypes.ResultsOur three-step filtering strategy rapidly removes 85.1% of all the pairwise sample comparisons and 91.0% of all pairwise sequence comparisons, accurately establishing which pairs of HCV samples are below the relatedness threshold.ConclusionsWe present a fast and efficient three-step filtering strategy that removes most sequence comparisons and accurately establishes transmission links of any threshold-based method. This highly efficient workflow will allow a faster response and molecular detection capacity, improving the rate of detection of viral transmissions with molecular data.

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