SeqAn3 3.4.0-rc.1
The Modern C++ library for sequence analysis.
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Data structures and approximate string search algorithms for large collection of text (e.g. DNA). More...
Modules | |
Configuration | |
Data structures and utility functions for configuring search algorithm. | |
DREAM Index | |
Provides seqan3::interleaved_bloom_filter. | |
FM Index | |
Provides seqan3::fm_index and seqan3::bi_fm_index as well as respective cursors. | |
k-mer Index | |
Implementation of shapes for a k-mer Index. | |
Views | |
Search related views. | |
Classes | |
class | seqan3::search_result< query_id_type, cursor_type, reference_id_type, reference_begin_position_type > |
The result class generated by the seqan3::seach algorithm. More... | |
struct | seqan3::search_result_printer< search_result< specs_t... > > |
The printer used for formatted output of the search result. More... | |
Functions | |
template<typename index_t , std::ranges::forward_range queries_t, typename configuration_t = decltype(search_cfg::default_configuration)> requires std::ranges::forward_range<std::ranges::range_reference_t<queries_t>> && std::same_as<range_innermost_value_t<queries_t>, typename index_t::alphabet_type> | |
auto | seqan3::search (queries_t &&queries, index_t const &index, configuration_t const &cfg=search_cfg::default_configuration) |
Search a query or a range of queries in an index. | |
Data structures and approximate string search algorithms for large collection of text (e.g. DNA).
Searching is a key component in many sequence analysis tools. The search module is a powerful and easy way to search sequences in a large text or an arbitrary nested collection of texts. When it comes to searching, indices are a core component for searching large amounts of data and are used for tools such as read mappers, assemblers or protein search tools.
SeqAn currently implements only the FM index and a k-mer index is planned. The FM index works with arbitrary pattern lengths and error numbers.
The Search module offers a unified search interface seqan3::search. The function chooses the best search method based on the provided index and an optional configuration.
The seqan3::search
algorithm can be configured in multiple ways. You simply pass a respective config
object to the algorithm:
The search configuration is independent of the index you use. It specifies for example how many mismatches and indels are allowed to consider your search successful.
See detailed documentation on Configuration for details.
The result is a range over "hits". We call a hit the position of a successful search. A hit in SeqAn is represented by the seqan3::search_result. You can configure what's part of the search result with the 4. Output Configuration.
You can iterate over the results with a normal for loop:
In order to choose the right Index for your use case, have a look at the next section (Available Indices for the seqan3::search). The documentation of the respective index will lead you to a more detailed description on how to use our search algorithm.
The seqan3::fm_index implements an original FM Index with a trivial backtracking approach. It is recommended for searches with no errors (exact searches). For exact searches, the original FM index is slightly faster than the bidirectional FM Index, and in general, it is only half the size.
The seqan3::bi_fm_index is a bidirectional FM index [1]. It improves the original FM index by allowing to extend the query to the right and the left. This makes the bidirectional FM index more efficient than its predecessor when searching with errors (approximate search). The performance gain is enabled by using (optimum) search schemes. Currently, using search schemes is only supported for searches with up to three errors and falls back to a trivial backtracking approach for higher errors. In the future, we plan to improve the search schemes to handle higher error counts.
Reference:
[1] Optimum Search Schemes for Approximate String Matching Using Bidirectional FM-Index. bioRxiv, 301085. https://doi.org/10.1101/301085, Kianfar, K., Pockrandt, C., Torkamandi, B., Luo, H., & Reinert, K. (2018).
If you are not interested in the exact match of your query, but simply whether your query can be found or not (also called membership query or lookup), SeqAn offers efficient data structures for this purpose. Of course you could also use a simple hash map (e.g., std::unordered_map
) but depending on your data this can quickly become infeasible in terms of memory consumption and runtime.
Take a look at the seqan3::interleaved_bloom_filter. The following example shows how to use it to simultaneously query all regions of a genome for the occurrence of a specific query:
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inline |
Search a query or a range of queries in an index.
index_t | Type of the index. See Available Indices for the seqan3::search for an overview of our indices. |
queries_t | Must model std::ranges::random_access_range over the index's alphabet and std::ranges::sized_range. A range of queries must additionally model std::ranges::forward_range and std::ranges::sized_range. |
[in] | queries | A single query or a range of queries. |
[in] | index | String index to be searched. |
[in] | cfg | A configuration object specifying the search parameters (e.g. number of errors, error types, output format, etc.). |
Header File
#include <seqan3/search/search.hpp>
The search algorithm strongly depends on the index that is used. Please see Available Indices for the seqan3::search for an overview of our indices.
For more details on how to configure the search, please see the respective documentation: Configuration.
Each query with \(e\) errors takes \(O(|query|^e)\) where \(e\) is the maximum number of errors.
Strong exception guarantee if iterating the query does not change its state and if invoking a possible delegate specified in cfg
also has a strong exception guarantee; basic exception guarantee otherwise.