bio 1.3.1

// Copyright 2014-2016 Johannes Köster.
// Licensed under the MIT license (http://opensource.org/licenses/MIT)
// This file may not be copied, modified, or distributed
// except according to those terms.

#![doc(
    html_logo_url = "https://raw.githubusercontent.com/rust-bio/rust-bio/master/img/bioferris.svg",
    html_favicon_url = "https://raw.githubusercontent.com/rust-bio/rust-bio/master/img/bioferris.svg"
)]

//! # Rust-bio, a bioinformatics library for Rust.
//! This library provides implementations of many algorithms and data structures
//! that are useful for bioinformatics.
//! All provided implementations are rigorously tested via continuous
//! integration.
//!
//! For **getting started** with using `rust-bio`, see [the `Getting started` section below](#getting-started).
//! For navigating the documentation of the available modules, see [the `Modules` section below](#modules).
//! If you want to contribute to `rust-bio`, see [the `Contribute` section in the repo](https://github.com/rust-bio/rust-bio#contribute).
//!
//! Currently, rust-bio provides
//!
//! * most major pattern matching algorithms,
//! * a convenient alphabet implementation,
//! * pairwise alignment,
//! * suffix arrays,
//! * the [Burrows-Wheeler-transform (BWT)](https://www.semanticscholar.org/paper/A-Block-sorting-Lossless-Data-Compression-Algorithm-Burrows-Wheeler/af56e6d4901dcd0f589bf969e604663d40f1be5d)
//! * the [Full-text index in Minute space index (FM-index)](https://doi.org/10.1109/SFCS.2000.892127),
//! * FMD-Index for finding supermaximal exact matches,
//! * a q-gram index,
//! * utilities to work with [PSSMs](https://en.wikipedia.org/wiki/Position_weight_matrix),
//! * an open reading frame (ORF) search algorithm,
//! * a rank/select data structure,
//! * [serde](https://github.com/serde-rs/serde) support for all data structures when built with `nightly` feature,
//! * readers and writers for FASTQ, FASTA and BED,
//! * helper functions for combinatorics and dealing with log probabilities,
//! * an implementation of the Hidden Markov Model and related algorithms.
//!
//! For reading and writing SAM/BAM/CRAM, VCF/BCF files or tabix indexed files, have a look at [rust-htslib](https://docs.rs/rust-htslib).
//!
//! # Getting started
//!
//! We explain how to use Rust-Bio step-by-step.
//! Users who already have experience with Rust can skip right to [Step 3: Use Rust-Bio in your project](https://docs.rs/bio/#step-3-use-rust-bio-in-your-project).
//! Users who already know `rust-bio` might want to jump right into the [modules docs](https://docs.rs/bio/#modules)
//!
//! ## Step 1: Setting up Rust
//!
//! Rust can be installed following the instruction for [rustup](https://rustup.rs/).
//!
//!
//! ## Step 2: Setting up a new Rust project
//!
//! Since Rust-Bio is a library, you need to setup your own new Rust project to use Rust-Bio.
//! With Rust, projects and their dependencies are managed with the builtin package manager [Cargo](https://doc.rust-lang.org/cargo/index.html).
//! To create a new Rust project, issue
//!
//! ```bash
//! cargo new hello_world --bin
//! cd hello_world
//! ```
//! in your terminal. The flag `--bin` tells Cargo to create an executable project instead of a library.
//! In [this section](https://doc.rust-lang.org/nightly/book/hello-cargo.html#a-new-project) of the Rust docs, you find details about what Cargo just created for you.
//!
//! Your new project can be compiled with
//! ```bash
//! cargo build
//! ```
//! If dependencies in your project are out of date, update with
//! ```bash
//! cargo update
//! ```
//! Execute the compiled code with
//! ```bash
//! cargo run
//! ```
//! If you are new to Rust, we suggest to proceed with [learning Rust](https://www.rust-lang.org/learn) via the Rust docs.
//!
//! ## Step 3: Use Rust-Bio in your project
//!
//! To use Rust-Bio in your Rust project, add the following to your `Cargo.toml`
//!
//! ```toml
//! [dependencies]
//! bio = "*"
//! ```
//!
//! and import the crate from your source code:
//!
//! ```rust
//! extern crate bio;
//! ```
//!
//! ## Example: FM-index and FASTQ
//!
//! An example of using `rust-bio`:
//!
//! ```rust
//! // Import some modules
//! use bio::alphabets;
//! use bio::data_structures::bwt::{bwt, less, Occ};
//! use bio::data_structures::fmindex::{BackwardSearchResult, FMIndex, FMIndexable};
//! use bio::data_structures::suffix_array::suffix_array;
//! use bio::io::fastq;
//! use bio::io::fastq::FastqRead;
//! use std::io;
//!
//! // a given text
//! let text = b"ACAGCTCGATCGGTA$";
//! let pattern = b"ATCG";
//!
//! // Create an FM-Index for the given text.
//!
//! // instantiate an alphabet
//! let alphabet = alphabets::dna::iupac_alphabet();
//! // calculate a suffix array
//! let sa = suffix_array(text);
//! // calculate the Burrows-Wheeler-transform
//! let bwt = bwt(text, &sa);
//! // calculate the vectors less and Occ (occurrences)
//! let less = less(&bwt, &alphabet);
//! let occ = Occ::new(&bwt, 3, &alphabet);
//! // set up FMIndex
//! let fmindex = FMIndex::new(&bwt, &less, &occ);
//! // do a backwards search for the pattern
//! let interval = fmindex.backward_search(pattern.iter());
//! let mut partial_match_len = 0;
//! // get the locations where the pattern matched (completely in this case).
//! let positions = match interval {
//!     BackwardSearchResult::Complete(saint) => saint.occ(&sa),
//!     BackwardSearchResult::Partial(saint, l) => {
//!         partial_match_len = l;
//!         saint.occ(&sa)
//!     }
//!     BackwardSearchResult::Absent => Vec::new(),
//! };
//! // Iterate over a FASTQ file, use the alphabet to validate read
//! // sequences and search for exact matches in the FM-Index.
//!
//! // create FASTQ reader
//! let mut reader = fastq::Reader::new(io::stdin());
//! let mut record = fastq::Record::new();
//! let mut partial_match_len = 0;
//! reader.read(&mut record).expect("Failed to parse record");
//! while !record.is_empty() {
//!     let check = record.check();
//!     if check.is_err() {
//!         panic!("I got a rubbish record!")
//!     }
//!     // obtain sequence
//!     let seq = record.seq();
//!     // check, whether seq is in the expected alphabet
//!     if alphabet.is_word(seq) {
//!         let interval = fmindex.backward_search(seq.iter());
//!         // get the positions where seq matched completely
//!         // or where the maximal matching suffix of seq occurred.
//!         let positions = match interval {
//!             BackwardSearchResult::Complete(saint) => saint.occ(&sa),
//!             BackwardSearchResult::Partial(saint, l) => {
//!                 partial_match_len = l;
//!                 saint.occ(&sa)
//!             }
//!             BackwardSearchResult::Absent => Vec::new(),
//!         };
//!     }
//!     reader.read(&mut record).expect("Failed to parse record");
//! }
//! ```
//!
//! Documentation and further examples for each module can be found in the module descriptions below.
//!
//!
//! ## Example: Multithreaded
//!
//! ```rust
//! use bio::alphabets;
//! use bio::data_structures::bwt::{bwt, less, Occ};
//! use bio::data_structures::fmindex::{BackwardSearchResult, FMIndex, FMIndexable};
//! use bio::data_structures::suffix_array::suffix_array;
//! use std::sync::Arc;
//! use std::thread;
//!
//! let text = b"ACGGATGCTGGATCGGATCGCGCTAGCTA$";
//! let patterns = vec![b"ACCG", b"TGCT"];
//!
//! // Create an FM-Index for a given text.
//! let alphabet = alphabets::dna::iupac_alphabet();
//! let sa = suffix_array(text);
//! let bwt = Arc::new(bwt(text, &sa));
//! let less = Arc::new(less(bwt.as_ref(), &alphabet));
//! let occ = Arc::new(Occ::new(bwt.as_ref(), 3, &alphabet));
//! let fmindex = Arc::new(FMIndex::new(bwt, less, occ));
//!
//! // Spawn threads to perform backward searches for each interval
//! let interval_calculators = patterns
//!     .into_iter()
//!     .map(|pattern| {
//!         let fmindex = fmindex.clone();
//!         thread::spawn(move || fmindex.backward_search(pattern.iter()))
//!     })
//!     .collect::<Vec<_>>();
//!
//! // Loop through the results, extracting the positions array for each pattern
//! for interval_calculator in interval_calculators {
//!     let positions = match interval_calculator.join().unwrap() {
//!         BackwardSearchResult::Complete(saint) => saint.occ(&sa),
//!         _ => Vec::new(),
//!     };
//! }
//! ```
//!
//! Documentation and further examples for each module can be found in the module descriptions below.
//!
//! # Benchmarks
//!
//! Since Rust-Bio is based on a compiled language, similar performance to C/C++ based libraries can be expected. Indeed, we find the pattern matching algorithms of Rust-Bio to perform in the range of the C++ library Seqan:
//!
//! | Algorithm | Rust-Bio | Seqan   |
//! | --------- | -------: | ------: |
//! | BNDM      | 77ms     | 80ms    |
//! | Horspool  | 122ms    | 125ms   |
//! | BOM       | 103ms    | 107ms   |
//! | Shift-And | 241ms    | 545ms   |
//!
//! We measured 10000 iterations of searching pattern `GCGCGTACACACCGCCCG` in the sequence of the hg38 MT chromosome.
//! Initialization time of each algorithm for the given pattern was included in each iteration. Benchmarks were conducted with *Cargo bench* for Rust-Bio and *Python timeit* for Seqan on an Intel Core i5-3427U CPU.
//! Benchmarking Seqan from *Python timeit* entails an overhead of 1.46ms for calling a C++ binary. This overhead was subtracted from above Seqan run times.
//! Note that this benchmark only compares the two libraries to exemplify that Rust-Bio has comparable speed to C++ libraries: all used algorithms have their advantages for specific text and pattern structures and lengths (see [the pattern matching section in the documentation](https://docs.rs/bio/0.28.2/bio/pattern_matching/index.html))./!

#[macro_use]
extern crate approx;

#[macro_use]
extern crate custom_derive;

#[macro_use]
extern crate lazy_static;

#[macro_use]
extern crate newtype_derive;

#[macro_use]
extern crate serde_derive;

#[macro_use]
extern crate strum_macros;

#[macro_use]
extern crate getset;

#[cfg(feature = "phylogeny")]
#[macro_use]
extern crate pest_derive;

pub mod alignment;
pub mod alphabets;
pub mod data_structures;
pub mod io;
pub mod pattern_matching;
pub mod scores;
pub mod seq_analysis;
pub mod stats;
pub mod utils;
pub use bio_types;