Alignment- and reference-free phylogenomics with colored de-Bruijn graphs

Wittler R. Alignment- and reference-free phylogenomics with colored de-Bruijn graphs. arXiv:1905.04165. 2019.We present a new whole-genome based approach to infer large-scale phylogenies that is alignment- and reference-free. In contrast to other methods, it does not rely on pairwise comparisons to determine distances to infer edges in a tree. Instead, a colored de-Bruijn graph is constructed, and information on common subsequences is extracted to infer phylogenetic splits. Application to different datasets confirms robustness of the approach. A comparison to other state-of-the-art whole-genome based methods indicates comparable or higher accuracy and efficiency

Fast and Simple Jumbled Indexing for Binary Run-Length Encoded Strings

Important papers have appeared recently on the problem of indexing binary strings for jumbled pattern matching, and further lowering the time bounds in terms of the input size would now be a breakthrough with broad implications. We can still make progress on the problem, however, by considering other natural parameters. Badkobeh et al. (IPL, 2013) and Amir et al. (TCS, 2016) gave algorithms that index a binary string in O(n + r^2 log r) time, where n is the length and r is the number of runs, and Giaquinta and Grabowski (IPL, 2013) gave one that runs in O(n + r^2) time. In this paper we propose a new and very simple algorithm that also runs in O(n + r^2) time and can be extended either so that the index returns the position of a match (if there is one), or so that the algorithm uses only O(n) bits of space instead of O(n) words

Bloom Filter Trie - a data structure for pan-genome storage

Holley G, Wittler R, Stoye J. Bloom Filter Trie - a data structure for pan-genome storage. In: Pop M, Touzet H, eds. Algorithms in Bioinformatics. WABI 2015. Proceedings. Lecture Notes in Computer Science . Vol 9289. Berlin, Heidelberg: Springer; 2015: 217-230

Bloom Filter Trie: an alignment-free and reference-free data structure for pan-genome storage

Holley G, Wittler R, Stoye J. Bloom Filter Trie: an alignment-free and reference-free data structure for pan-genome storage. Algorithms for Molecular Biology. 2016;11(1): 3.Background High throughput sequencing technologies have become fast and cheap in the past years. As a result, large-scale projects started to sequence tens to several thousands of genomes per species, producing a high number of sequences sampled from each genome. Such a highly redundant collection of very similar sequences is called a pan-genome. It can be transformed into a set of sequences “colored” by the genomes to which they belong. A colored de Bruijn graph (C-DBG) extracts from the sequences all colored k-mers, strings of length k, and stores them in vertices. Results In this paper, we present an alignment-free, reference-free and incremental data structure for storing a pan-genome as a C-DBG: the bloom filter trie (BFT). The data structure allows to store and compress a set of colored k-mers, and also to efficiently traverse the graph. Bloom filter trie was used to index and query different pangenome datasets. Compared to another state-of-the-art data structure, BFT was up to two times faster to build while using about the same amount of main memory. For querying k-mers, BFT was about 52–66 times faster while using about 5.5–14.3 times less memory. Conclusion We present a novel succinct data structure called the Bloom Filter Trie for indexing a pan-genome as a colored de Bruijn graph. The trie stores k-mers and their colors based on a new representation of vertices that compress and index shared substrings. Vertices use basic data structures for lightweight substrings storage as well as Bloom filters for efficient trie and graph traversals. Experimental results prove better performance compared to another state-of-the-art data structure. Availability https://​www.​github.​com/​GuillaumeHolley/​BloomFilterTrie

Repeat- and error-aware comparison of deletions

Wittler R, Marschall T, Schönhuth A, Makinen V. Repeat- and error-aware comparison of deletions. Bioinformatics. 2015;31(18):2947-2954

Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes

Wittler R, Chauve C. Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes. BMC Bioinformatics. 2011;12(Suppl. 9):S21

Correction: Unraveling overlapping deletions by agglomerative clustering

Wittler R. Correction: Unraveling overlapping deletions by agglomerative clustering. BMC Genomics. 2013;14(Suppl 1): S16