113,609 research outputs found
SVIM: Structural Variant Identification using Mapped Long Reads
Motivation: Structural variants are defined as genomic variants larger than 50bp. They have been shown to affect more bases in any given genome than SNPs or small indels. Additionally, they have great impact on human phenotype and diversity and have been linked to numerous diseases. Due to their size and association with repeats, they are difficult to detect by shotgun sequencing, especially when based on short reads. Long read, single molecule sequencing technologies like those offered by Pacific Biosciences or Oxford Nanopore Technologies produce reads with a length of several thousand base pairs. Despite the higher error rate and sequencing cost, long read sequencing offers many advantages for the detection of structural variants. Yet, available software tools still do not fully exploit the possibilities. Results: We present SVIM, a tool for the sensitive detection and precise characterization of structural variants from long read data. SVIM consists of three components for the collection, clustering and combination of structural variant signatures from read alignments. It discriminates five different variant classes including similar types, such as tandem and interspersed duplications and novel element insertions. SVIM is unique in its capability of extracting both the genomic origin and destination of duplications. It compares favorably with existing tools in evaluations on simulated data and real datasets from PacBio and Nanopore sequencing machines. Availability and implementation: The source code and executables of SVIM are available on Github: github.com/eldariont/svim. SVIM has been implemented in Python 3 and published on bioconda and the Python Package Index. Supplementary information: Supplementary data are available at Bioinformatics online
Representing Isabelle in LF
LF has been designed and successfully used as a meta-logical framework to
represent and reason about object logics. Here we design a representation of
the Isabelle logical framework in LF using the recently introduced module
system for LF. The major novelty of our approach is that we can naturally
represent the advanced Isabelle features of type classes and locales.
Our representation of type classes relies on a feature so far lacking in the
LF module system: morphism variables and abstraction over them. While
conservative over the present system in terms of expressivity, this feature is
needed for a representation of type classes that preserves the modular
structure. Therefore, we also design the necessary extension of the LF module
system.Comment: In Proceedings LFMTP 2010, arXiv:1009.218
The C++0x "Concepts" Effort
C++0x is the working title for the revision of the ISO standard of the C++
programming language that was originally planned for release in 2009 but that
was delayed to 2011. The largest language extension in C++0x was "concepts",
that is, a collection of features for constraining template parameters. In
September of 2008, the C++ standards committee voted the concepts extension
into C++0x, but then in July of 2009, the committee voted the concepts
extension back out of C++0x.
This article is my account of the technical challenges and debates within the
"concepts" effort in the years 2003 to 2009. To provide some background, the
article also describes the design space for constrained parametric
polymorphism, or what is colloquially know as constrained generics. While this
article is meant to be generally accessible, the writing is aimed toward
readers with background in functional programming and programming language
theory. This article grew out of a lecture at the Spring School on Generic and
Indexed Programming at the University of Oxford, March 2010
Parametric Compositional Data Types
In previous work we have illustrated the benefits that compositional data
types (CDTs) offer for implementing languages and in general for dealing with
abstract syntax trees (ASTs). Based on Swierstra's data types \'a la carte,
CDTs are implemented as a Haskell library that enables the definition of
recursive data types and functions on them in a modular and extendable fashion.
Although CDTs provide a powerful tool for analysing and manipulating ASTs, they
lack a convenient representation of variable binders. In this paper we remedy
this deficiency by combining the framework of CDTs with Chlipala's parametric
higher-order abstract syntax (PHOAS). We show how a generalisation from
functors to difunctors enables us to capture PHOAS while still maintaining the
features of the original implementation of CDTs, in particular its modularity.
Unlike previous approaches, we avoid so-called exotic terms without resorting
to abstract types: this is crucial when we want to perform transformations on
CDTs that inspect the recursively computed CDTs, e.g. constant folding.Comment: In Proceedings MSFP 2012, arXiv:1202.240
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