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

Mapping Short Reads to a Genomic Sequence with Circular Structure

In this paper, we introduce our efficient simple method which can locate all occurrences of pattern P of k subpatterns with “don’t cares” of length m in text S of length n. Our algorithm employs advanced data structure and the Kangaroo method, which can be applied to selected suffixes of the suffix tree of S to answer subsequent queries in O(k) time using a predefined computational method to find all occurrences of pattern P with “don’t cares” in text S in a fast and effective manner.

Detecting Pattern Efficiently with Don’t Cares

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Special Issue of Algorithmica for the 28th London Stringology Days & London Algorithmic Workshop (LSD & LAW)

Within the last decades, the dead-zone algorithms have emerged as being highly performant on certain types of data. Such algorithms solve the keyword exact matching problem over strings, though extensions to trees and two-dimensional data have also been devised. In this short paper, we give an overview of such algorithms.

A Brief Overview of Dead-Zone Pattern Matching Algorithms

Given a weighted sequence X of length n and an integer constant λ, the minimum λ-cover problem of weighted sequences is to find the sets of λ factors of X each of equal length such that the set covers X, and the length of each element in the set is minimum. By constructing the Equivalence Class Tree and iteratively computing the occurrences of a set of factors in weighted sequences, we tackle the problem in O(n 2) time for constant alphabet size.

Costas S. Iliopoulos

Papers

Mapping Short Reads to a Genomic Sequence with Circular Structure

Detecting Pattern Efficiently with Don’t Cares

Special Issue of Algorithmica for the 28th London Stringology Days & London Algorithmic Workshop (LSD & LAW)

A Brief Overview of Dead-Zone Pattern Matching Algorithms

Computing the Minimum λ-Cover in Weighted Sequences