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# nthash1, pure python implementation | |
# | |
# (not nthash2, so only for k values < 64) | |
# ported from https://github.com/luizirber/nthash/ | |
h = {'A': 0x3c8b_fbb3_95c6_0474, | |
'C': 0x3193_c185_62a0_2b4c, | |
'G': 0x2032_3ed0_8257_2324, | |
'T': 0x2955_49f5_4be2_4456, | |
'N': 0} |
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#!/usr/bin/env python | |
doc = """ | |
Quickly estimates insert sizes of read datasets, given some sequence(s) they can be mapped to. | |
Author: Rayan Chikhi | |
short usage: <reference> <*.fastq> | |
example: | |
estimate-insert-sizes contigs.fa readsA_1.fq readsA_2.fq readsB_1.fq readsB_2.fq |
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Wheeler graphs | |
Gagie, Manzini, Siren | |
Theoretical Computer Science, 2017 | |
https://www.sciencedirect.com/science/article/pii/S0304397517305285 | |
Notes of a whiteboard presentation to the Bonsai team in Lille. | |
These notes largely follow the paper. | |
Rayan Chikhi, 2019 |
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This document is a partial presentation of the RopeBWT2 pre-print | |
http://arxiv.org/abs/1406.0426 | |
It is the transcript of a presentation made within the Medvedev group at Penn State | |
in July 2014. It focuses on illustrating some notions from the methods, and | |
illustrating and proving Algorithm 1. While this document does not cover the main | |
contribution of the RopeBWT2 paper, I hope it can be helpful towards understanding | |
the theoretical foundations that led to Algorithms 2 and 3. |