There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

A graph labeling is an assignment of integers to the vertices or edges, or both, subject to certain conditions. Graph labelings were first introduced in the late 1960s. In the intervening years dozens of graph labelings techniques have been studied in over 1000 papers. Finding out what has been done for any particular kind of labeling and keeping up with new discoveries is difficult because of the sheer number of papers and because many of the papers have appeared in journals that are not widely available. In this survey I have collected everything I could find on graph labeling. For the convenience of the reader the survey includes a detailed table of contents and index.

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A Dynamic Survey of Graph Labeling

DNA sequencing with chain terminating inhibitors

The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p −12 ) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants

Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists

This book constitutes the refereed proceedings of the 22nd International Symposium on String Processing and Information Retrieval, SPIRE 2015, held in London, UK, in September 2015. The 28 full and 6 short papers included in this volume were carefully reviewed and selected from 90 submissions. The papers cover research in all aspects of string processing, information retrieval, computational biology, pattern matching, semi-structured data, and related applications.

String Processing and Information Retrieval: 22nd International Symposium, SPIRE 2015, London, UK, September 1-4, 2015, Proceedings

https://hal-upec-upem.archives-ouvertes.fr/hal-00836960/document

The maximal number of cubic runs in a word

In this paper, we revisit the much studied problem of Pattern matching with Swaps (Swap Matching problem, for short). We first present a new graph-theoretic approach to model the problem, which opens a new and so far unexplored avenue to solve the problem. Then, using the model, we devise an efficient algorithm to solve the swap matching problem. The resulting algorithm is an adaptation of the classic shiftor algorithm. For patterns having length similar to the word-size of the target machine, the algorithm runs in O(n log m) time, where n and m are the length of the text and the pattern respectively.

A new model to solve the swap matching problem and efficient algorithms for short patterns

Weighted sequences have been recently introduced as a tool to handle a set of sequences that are not identical but have many local similarities. The weighted sequence is a statistical image of this set, where the probability of every symbol's occurrence at every text location is given. We address the problem of approximately matching a pattern in such a weighted sequence. The pattern is a given string and we seek all locations in the set where the pattern occurs with a high enough probability. We define the notion of Hamming distance and edit distance in weighted sequences and give efficient algorithms for computing them. We compute two versions of the Hamming distance in time O(n√m log m ), where n is the length of the weighted text and m is the pattern length. The edit distance is computed in time O(nm) and O(nm 2 ), depending on the edit distance definition used. Unfortunately, due to space considerations, the edit distance details are left to the journal version. We also define the notion of weighted matching in infinite alphabets and show that exact weighted matching can be computed in time O(slog 2 s), where s is the number of text symbols having non-zero probability. The weighted Hamming distance over infinite alphabets can be computed in time min(O(kn√s + s 3/2 log 2 s), O(s 4/3 m 1/3 log s)).

Costas S. Iliopoulos

Papers

String Processing and Information Retrieval: 22nd International Symposium, SPIRE 2015, London, UK, September 1-4, 2015, Proceedings

The maximal number of cubic runs in a word

A new model to solve the swap matching problem and efficient algorithms for short patterns

Approximate matching in weighted sequences

Efficient algorithms for three variants of the LPF table