Push rxrxptuxltlp #12
@@ -24,3 +24,37 @@ Sauf qu'avec un index approximatif, les résultats seront approximatifs.
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--detail et --mismatch à implementer
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- status : affiche le statut de l'index
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## Problème biologique sur l'identification des contaminants
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Exemple de reads problématiques:
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```
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>LH00534:161:22WMGWLT4:4:1101:45301:1420 {"coverage":{"gbbct":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]},"kmer_count":117,"kmer_strict_matches":{"gbbct":117}}
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GCCCCTACCGTACTCCAGCTTGGTAGTTTCCACCGCCTGTCCAGGGTTGAGCCCTGGGATTTGACGGCGGACTTAAAAAGCCACCTACAGACGCTTTACGCCCAATCATTCCGGATAACGCTTGCATCCTCTGTATTACCGCGGCTGCTGG
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```
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Par blast match une quantité invréssemblable de genomes chloroplastique avec un match de 100% (6554 hits pour Streptophyta)
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mais aussi une quantité de sequences importantes à des OTU bactériennes (uncutured bacteria 115 hits) aussi avec 100% de similarité.
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```
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Uncultured bacterium clone Otu01032 16S ribosomal RNA gene, partial sequence
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Sequence ID: KX996137.1Length: 440Number of Matches: 1
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Range 1: 153 to 303GenBankGraphics
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Next Match
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Previous Match
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Alignment statistics for match #1 Score Expect Identities Gaps Strand
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273 bits(302) 2e-69 151/151(100%) 0/151(0%) Plus/Minus
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Query 1 GCCCCTACCGTACTCCAGCTTGGTAGTTTCCACCGCCTGTCCAGGGTTGAGCCCTGGGAT 60
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Sbjct 303 GCCCCTACCGTACTCCAGCTTGGTAGTTTCCACCGCCTGTCCAGGGTTGAGCCCTGGGAT 244
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Query 61 TTGACGGCGGACTTAAAAAGCCACCTACAGACGCTTTACGCCCAATCATTCCGGATAACG 120
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Sbjct 243 TTGACGGCGGACTTAAAAAGCCACCTACAGACGCTTTACGCCCAATCATTCCGGATAACG 184
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Query 121 CTTGCATCCTCTGTATTACCGCGGCTGCTGG 151
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Sbjct 183 CTTGCATCCTCTGTATTACCGCGGCTGCTGG 153
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```
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@@ -230,16 +230,11 @@ fn process_chunk(
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let batch = QueryBatch::from_records(records, k, 6, 0.7);
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let n_seqs = batch.ids.len();
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let mut accs: Vec<SeqAcc> =
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(0..n_seqs).map(|_| SeqAcc::new(n_genomes)).collect();
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let mut cov: Vec<Vec<Vec<u32>>> = if detail {
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batch.n_kmers.iter()
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.map(|&n| vec![vec![0u32; n as usize]; n_genomes])
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.collect()
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} else {
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Vec::new()
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};
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// Per-sequence s-mer result vectors in global sequence position order.
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// All partitions fill into this structure before Findere is applied.
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let mut seq_results: Vec<Vec<Option<Box<[u32]>>>> = batch.n_kmers.iter()
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.map(|&n| vec![None; n as usize])
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.collect();
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let by_part = batch.split_by_partition(n_partitions);
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@@ -256,38 +251,60 @@ fn process_chunk(
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std::process::exit(1);
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});
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let presence = force_presence || !with_counts;
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let threshold = presence_threshold;
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for (rsk, sk_kmer_results) in part_sks.iter().zip(kmer_results.iter()) {
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let filtered = apply_findere(sk_kmer_results, effective_z, n_genomes);
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let descs = batch.map.get(*rsk).expect("rsk must be in map");
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for desc in descs {
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let acc = &mut accs[desc.seq_idx as usize];
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let offset = desc.kmer_offset as usize;
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let dst = &mut seq_results[desc.seq_idx as usize];
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for (j, hit) in sk_kmer_results.iter().enumerate() {
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dst[offset + j] = hit.as_ref().map(|r| r.clone());
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}
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}
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}
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}
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for (local_pos, hit) in filtered.iter().enumerate() {
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match hit {
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None => {
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if sk_kmer_results[local_pos].is_none() {
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acc.kmer_missing += 1;
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}
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}
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Some(row) => {
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acc.kmer_count += 1;
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for (g, &v) in row.iter().enumerate() {
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if v == 0 { continue; }
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let contribution = if presence {
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u32::from(v >= threshold)
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} else {
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v
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};
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acc.genome_totals[g] += contribution;
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if detail {
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let abs_pos = desc.kmer_offset as usize + local_pos;
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cov[desc.seq_idx as usize][g][abs_pos] += contribution;
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}
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}
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// Apply Findere on each complete sequence vector, then accumulate.
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let n_kmers_out: Vec<usize> = batch.n_kmers.iter()
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.map(|&n| { let n = n as usize; if n >= effective_z { n - effective_z + 1 } else { 0 } })
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.collect();
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let mut accs: Vec<SeqAcc> =
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(0..n_seqs).map(|_| SeqAcc::new(n_genomes)).collect();
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let mut cov: Vec<Vec<Vec<u32>>> = if detail {
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n_kmers_out.iter()
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.map(|&n| vec![vec![0u32; n]; n_genomes])
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.collect()
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} else {
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Vec::new()
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};
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let presence = force_presence || !with_counts;
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let threshold = presence_threshold;
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for seq_idx in 0..n_seqs {
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let filtered = apply_findere(&seq_results[seq_idx], effective_z, n_genomes);
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let acc = &mut accs[seq_idx];
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for (pos, hit) in filtered.iter().enumerate() {
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match hit {
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None => {
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if seq_results[seq_idx][pos].is_none() {
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acc.kmer_missing += 1;
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}
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}
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Some(row) => {
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acc.kmer_count += 1;
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for (g, &v) in row.iter().enumerate() {
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if v == 0 { continue; }
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let contribution = if presence {
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u32::from(v >= threshold)
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} else {
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v
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};
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acc.genome_totals[g] += contribution;
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if detail {
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cov[seq_idx][g][pos] += contribution;
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}
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}
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}
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Block a user