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

We present new faster algorithms for the problems of δ and (δ, γ)-matching on numeric strings. In both cases the running time of the proposed algorithms is shown to be O(δn log m), where m is the pattern length, n is the text length and δ a given integer. Our approach makes use of Fourier transform methods and the running times are independent of the alphabet size. $O(n\sqrt{m\log{m}})$ algorithms for the γ-matching and total-difference problems are also given. In all the above cases, we improve existing running time bounds in the literature.

Faster algorithms for δ,γ-matching and related problems

Algorithms for computing variants of the longest common subsequence problem

The Longest Common Subsequence (LCS) problem is a classic and well-studied problem in computer science. The LCS problem is a common task in DNA sequence analysis with many applications to genetics and molecular biology. In this paper, we present a new and efficient algorithm for solving the LCS problem for two strings. Our algorithm runs in O(ℛlog log n+n) time, where ℛ is the total number of ordered pairs of positions at which the two strings match.

A New Efficient Algorithm for Computing the Longest Common Subsequence

Approximate periods of strings

Pattern Matching with Properties (Property Matching, for short), involves a string matching between the pattern and the text, and the requirement that the text part satisfies some property. It is straightforward to do sequential matching in a text with properties. However, indexing in a text with properties becomes difficult if we desire the time to be output dependent. We present an algorithm for indexing a text with properties in O(n log|Σ + n log log n) time for preprocessing and O(|P| log |Σ| + tocc π ) per query, where n is the length of the text, P is the sought pattern, E is the alphabet, and tocc π is the number of occurrences of the pattern that satisfy some property π. As a practical use of Property Matching we show how to solve Weighted Matching problems using techniques from Property Matching. Weighted sequences have been 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 we are given the probability of every symbol's occurrence at every text location. Weighted matching problems are pattern matching problems where the given text is weighted. We present a reduction from Weighted Matching to Property Matching that allows off-the-shelf solutions to numerous weighted matching problems including indexing, swapped matching, parameterized matching, approximate matching, and many more. Assuming that one seeks the occurrence of pattern P with probability ∈ in weighted text T of length n, we reduce the problem to a property matching problem of pattern P in text T' of length O(n(1 6) 2 log 1 6).

Costas S. Iliopoulos

Papers

Faster algorithms for δ,γ-matching and related problems

Algorithms for computing variants of the longest common subsequence problem

A New Efficient Algorithm for Computing the Longest Common Subsequence

Approximate periods of strings

Property matching and weighted matching