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.

/pdf/a-dynamic-survey-of-graph-labeling-198znmwxtq.pdf

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 efficient algorithms for storing past segments of a text. They are computed using two previously computed read-only arrays (SUF and LCP) composing the Suffix Array of the text. They compute the maximal length of the previous factor (subword) occurring at each position of the text in a table called LPF. This notion is central both in many conservative text compression techniques and in the most efficient algorithms for detecting motifs and repetitions occurring in a text.

The main results are: a linear-time algorithm that computes explicitly the permutation that transforms the LCP table into the LPF table; a time-space optimal computation of the LPF table; and an O(nlogn) strong in-place computation of the LPF table.

/pdf/lpf-computation-revisited-3mxuswtk1t.pdf

LPF Computation Revisited

Novel high-throughput (Deep) sequencing technology methods have redefined the way genome sequencing is performed. They are able to produce tens of millions of short sequences (reads) in a single experiment and with a much lower cost than previous sequencing methods. In this paper, we present a new algorithm for addressing the problem of efficiently mapping millions of short reads to a reference genome. In particular, we define and solve the Massive Approximate Pattern Matching problem for mapping short sequences to a reference genome.

A Fast and Efficient Algorithm for Mapping Short Sequences to a Reference Genome

Motivation: The constant advances in sequencing technology are turning whole-genome sequencing into a routine procedure, resulting in massive amounts of data that need to be processed. Tens of gigabytes of data in the form of short reads need to be mapped back to reference sequences, a few gigabases long. A first generation of short read alignment software successfully employed hash tables, and the current second generation uses Burrows-Wheeler Transform, further improving mapping speed. However, there is still demand for faster and more accurate mapping.Results: In this paper, we present REad ALigner, an efficient, accurate and consistent tool for aligning short reads obtained from next generation sequencing. It is based on a new, simple, yet efficient mapping algorithm that can match and outperform current BWT-based software.

REAL: an efficient REad ALigner for next generation sequencing reads

Parallel RAM algorithms for factorizing words

https://hal-upec-upem.archives-ouvertes.fr/hal-01806293/document

Costas S. Iliopoulos

Papers

LPF Computation Revisited

A Fast and Efficient Algorithm for Mapping Short Sequences to a Reference Genome

REAL: an efficient REad ALigner for next generation sequencing reads

Parallel RAM algorithms for factorizing words

Order-preserving indexing