Assembly pipeline for 10X chromium genomes of Mytilus species
Assembly pipeline from 10x chromium reads from the preprint "Three new genome assemblies of blue mussel lineages: North and South European Mytilus edulis and Mediterranean Mytilus galloprovincialis" bioRxiv ( https://doi.org/10.1101/2022.09.02.506387 ).
snakemake
(in a conda environnement for example) and
singularity
need to be installed.
Supernova storage workarounds
Supernova use large amount of storage for temporary and final results.
The supernova results are stored on a distant NAS that needs to be mounted first on my system.
sshfs nas4:/share/sea/sea/projects/ref_genomes/assembly_10x/results/supernova_assemblies \
results/supernova_assemblies \
-o idmap=user,compression=no,uid=1000,gid=1000,allow_root
I also used a 4T disk as a temporary local storage for supernova computation
sudo mount /dev/sd[x]1 /data/ref_genomes/assembly_10x/tmp
How to run
To run use:
conda activate snake_env
snakemake --use-conda \
--use-singularity --singularity-args "-B /nas_sea:/nas_sea" \
-j {threads} \
[either all_v6, asm_improvement, stats, repeats, annotation, finalize or ncbi_submission (see workflow/Snakefile)]
Code Snippets
12 13 14 15 16 17 18 | shell: """ zcat {input} > {output[0]} mkdir {output[1]} splitMfasta.pl {output[0]} \ --outputpath={output[1]} --minsize={params.split_size} """ |
27 28 29 30 | shell: """ augustus --gff3=on --species=caenorhabditis {input} > {output} """ |
45 46 | shell: "cat {input} | join_aug_pred.pl > {output}" |
70 71 72 73 74 75 76 | shell: """ run_rcorrector.pl -1 {input[0]} -2 {input[1]} \ -t {threads} \ -od {params.outdir} \ > {log} 2>&1 """ |
94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | shell: """ trim_galore --cores {threads} \ --phred33 \ --quality 20 \ --stringency 1 \ -e 0.1 \ --length 70 \ --output_dir {params.outdir} \ --basename {params.basename} \ --dont_gzip \ --paired \ {input} \ > {log} 2>&1 """ |
120 121 | shell: "bwa-mem2 index {input}" |
138 139 140 141 142 | shell: """ bwa-mem2 mem -t {threads} {input[2]} {input[0]} {input[1]} 2> {log} \ | samtools view -b -@ {threads} -o {output} """ |
160 161 162 163 164 165 | shell: """ samtools merge -@ {threads} {params.tmp_merge} {input} samtools sort -@ {threads} -n -o {output} {params.tmp_merge} rm {params.tmp_merge} """ |
184 185 186 187 188 189 190 191 192 193 194 195 | shell: """ python /opt/agouti/agouti.py scaffold \ -assembly {input.fa} \ -bam {input.bam} \ -gff {input.gff} \ -outdir {params.outdir} \ -minMQ {params.minMQ} -maxFracMM {params.maxFracMM} \ > {log} 2>&1 gzip -c {params.outdir}/agouti.agouti.fasta > {output} """ |
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | run: import subprocess import json if os.path.exists(output.fasta): os.remove(output.fasta) # Get clusters for Mollusca taxid=6447 url = 'http://www.orthodb.org/search?level=6447&limit=50000' cmd = f'wget "{url}" -O {output.clustids}' subprocess.run(cmd, shell=True, check=True, stderr=subprocess.DEVNULL) # Loop for each cluster with open(output.clustids, 'r') as fr: clusters = json.load(fr) for C_id in clusters['data']: url = f'http://www.orthodb.org/fasta?id={C_id}&species=all' cmd = f'wget "{url}" -O - >> {output.fasta}' subprocess.run(cmd, shell=True, check=True, stderr=subprocess.DEVNULL) |
42 43 44 45 46 47 48 | shell: """ zcat {input} > tmp_ref.{wildcards.asm}.fa hisat2-build tmp_ref.{wildcards.asm}.fa {params.index_prefix} \ > {log} 2>&1 rm tmp_ref.{wildcards.asm}.fa """ |
71 72 73 74 75 76 77 78 79 | shell: """ hisat2 -p {threads} \ -x {params.index_prefix} \ -1 {input.reads[0]} -2 {input.reads[1]} \ 2> {log} | \ samtools sort -@ {threads} -O BAM -o {output[0]} samtools index {output} """ |
117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | shell: """ find {params.out_dir} -name '{params.to_rm}' -type d -delete cp /opt/gm_key_64 ~/.gm_key braker.pl \ --genome {input.genome} \ --prot_seq {input.prot_db} \ --bam {params.list_bams} \ --workingdir {params.out_dir} \ --species {params.species} \ --useexisting \ --etpmode --softmasking --cores {threads} \ --gff3 \ --GENEMARK_PATH {params.genemark_path} \ --PROTHINT_PATH {params.genemark_path}/ProtHint/bin \ > {log} 2>&1 """ |
157 158 159 160 161 162 | shell: """ gff3_to_fasta -g {input.gff} -f {input.fa} \ -st pep -d simple -o {params.prefix} \ > log 2>&1 """ |
174 175 176 177 178 | shell: """ mantis setup -c {threads} > {log} 2>&1 touch {output} """ |
195 196 197 198 199 200 201 202 203 204 205 206 | shell: """ rm -r {params.outdir} mantis run \ -i {input.pep} \ -o {params.outdir} \ -od '{params.org_detail}' \ -km \ -gff \ -c {threads} \ > {log} 2>&1 """ |
20 21 22 23 | shell: """ zcat {input} > {output} """ |
33 34 | shell: "wget -O {output} {params.link}" |
48 49 50 51 52 53 | shell: """ minimap2 -t {threads} -x map-ont \ {input.ref} {input.ont_reads} \ > {output} 2> {log} """ |
70 71 72 73 74 75 76 77 78 79 80 81 82 83 | shell: """ java -jar {input.lrscaf} \ -c {input.ref} \ -a {input.paf} \ -o {params.res}/res \ -micl 1000 \ -t mm \ -i 0.3 \ -p {threads} \ > {log} 2>&1 cp {params.res}/res/scaffolds.fasta {output} """ |
99 100 101 102 103 104 105 106 107 108 109 | shell: """ ragtag.py scaffold \ {input.ref_coruscus} \ {input.draft_assembly} \ -o {params.out_dir} \ --mm2-params '-x asm10 -t {threads}' -u \ > {log} 2>&1 cp {params.out_dir}/ragtag.scaffolds.fasta {output} """ |
124 125 126 127 128 129 130 131 | shell: """ minimap2 -t {threads} -ax map-ont \ {input.ref} {input.ont_reads} 2> {log} | \ samtools sort -@ {threads} | \ samtools view -b -@ {threads} -o {output} samtools index {output} """ |
154 155 156 157 158 159 160 161 162 163 164 165 | shell: """ java -Xmx{params.java_mem}G -jar {input.pilon} \ --genome {input.ref} \ --frags {input.pe_bam} \ --nanopore {input.ont_bam} \ --targets {input.target} \ --output {params.prefix} \ --outdir {params.out_dir} \ --changes --vcf --tracks --diploid --fix all \ > {log} 2>&1 """ |
184 185 186 187 188 189 190 191 192 193 194 | shell: """ ragtag.py scaffold \ {input.ref_coruscus} \ {input.draft_assembly} \ -o {params.out_dir} \ --mm2-params '-x asm10 -t {threads}' -u \ > {log} 2>&1 cp {params.out_dir}/ragtag.scaffolds.fasta {output} """ |
202 203 | shell: "wget -O {output} {params.link}" |
212 213 214 215 | shell: """ bwa-mem2 index {input} """ |
243 244 245 246 247 248 249 250 | shell: """ [ ! -e {input.ref}.0123 ] && bwa-mem2 index {input.ref} bwa-mem2 mem -t {threads} {input.ref} {input.R1} {input.R2} 2> {log} | \ samtools sort -@ {threads} | \ samtools view -b -@ {threads} -o {output} samtools index {output} """ |
262 263 264 265 266 267 268 269 | shell: """ for i in {{1..14}}; do grep '>' {input.ref} | sed 's/>//' | awk -v record=$i 'NR==record {{print $0}}' \ > {params.out_dir}/target_$(printf '%02d' $i).txt done grep '>' {input.ref} | tail -n +15 | sed 's/>//' > {output[14]} """ |
293 294 295 296 297 298 299 300 301 302 303 | shell: """ java -Xmx{params.java_mem}G -jar {input.pilon} \ --genome {input.ref} \ --frags {input.pe_bam} \ --targets {input.target} \ --output {params.prefix} \ --outdir {params.out_dir} \ --changes --vcf --tracks --diploid --fix all \ > {log} 2>&1 """ |
323 324 | shell: "cat {input} | bgzip -c > {output}" |
12 13 14 15 16 17 | shell: """ seqkit grep -v -s -r -p '^N+$' {input.fa} > {output.fa} ln -sr {input[1]} results/blobtoolkit/{wildcards.sample}_{wildcards.version}/GM_1.fastq.gz ln -sr {input[2]} results/blobtoolkit/{wildcards.sample}_{wildcards.version}/GM_2.fastq.gz """ |
25 26 27 28 29 30 31 32 33 | shell: """ cp {input.conf} {output.conf} SC=$( grep '>' {input.fa} | wc -l ) SP=$( grep -v '>' {input.fa} | wc -m ) sed -i "s/scaffold-count.*/scaffold-count:\ $SC/" {output.conf} sed -i "s/span.*/span:\ $SP/" {output.conf} sed -i "s/prefix.*/prefix:\ {wildcards.sample}_{wildcards.version}/" {output.conf} """ |
49 50 51 52 53 54 55 56 57 58 59 60 | shell: """ snakemake -p \ -s /opt/blobtoolkit/insdc-pipeline/Snakefile \ --directory {params.dir} \ --use-conda \ --conda-prefix .conda \ --configfile {input.conf} \ -j {threads} \ --latency-wait 30 \ --resources btk=1 """ |
75 76 77 78 79 80 81 | shell: """ {params.blobtools_bin} add \ --busco {input[2]} \ --busco {input[1]} \ {params.blobdir} """ |
97 98 99 100 101 102 103 | shell: """ cp {input[1]} {output[1]} cp -r {params.indir} results/blobtoolkit/blobdirs/ && \ tar -czf {output[2]} {params.tardir} && \ rm -r {params.tardir} """ |
9 10 11 12 13 14 | shell: """ cd {params.outdir} wget -c {params.metazoa} -O - | tar -xz wget -c {params.mollusca} -O - | tar -xz """ |
27 28 | shell: "zcat {input} > {output}" |
50 51 52 53 54 55 56 | shell: """ busco -f -m genome -i {params.fa} -o {params.outdir} \ -q -c {threads} \ -l {params.db} > {log} 2>&1 cp -r {params.outdir} results/busco/ && rm -r {params.outdir} """ |
72 73 74 75 76 77 | shell: """ python workflow/scripts/summarize_buscos.py \ -o {output} \ --files {input} """ |
14 15 | shell: "seqkit replace -p '\s.*$' -r '' {input} > {output}" |
35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | shell: """ echo "((((mgal_02,GCA900618805),medu_02),mtro_02),GCA017311375);" > {params.seqfile} echo "*GCA017311375 {input[0]}" >> {params.seqfile} echo "mtro_02 {input[1]}" >> {params.seqfile} echo "medu_02 {input[2]}" >> {params.seqfile} echo "mgal_02 {input[3]}" >> {params.seqfile} echo "GCA900618805 {input[4]}" >> {params.seqfile} cactus \ {params.restart} \ --maxCores {threads} \ results/cactusJobStore \ {params.seqfile} \ {output} \ > {log} 2>&1 """ |
20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | shell: """ echo '{input.bams}' | tr ' ' '\\n' > results/calling/bam.list angsd sites index {input.targets} > {log} 2>&1 angsd -P {threads} \ -bam results/calling/bam.list \ -ref {input.ref} \ -sites {input.targets} \ -remove_bads 1 -uniqueOnly 1 -minMapQ 20 -minQ 20 \ -gl 2 -doMajorMinor 3 -doGlf 2 -doBcf 1 \ -doPost 1 -doMaf 1 -doGeno 1 -doCounts 1 \ -setMinChunkSize 10000 \ -out {params.prefix} \ >> {log} 2>&1 """ |
44 45 46 47 48 | shell: """ paste <(zcat {input[0]}) <(zcat {input[1]} | cut -f 4-) | \ bgzip -c > {output} """ |
66 67 68 69 70 71 72 73 74 75 76 77 78 79 | shell: """ python /opt/pcangsd/pcangsd.py \ -beagle {input} \ -minMaf {params.min_maf} \ -iter 200 \ -e {params.eigenvectors} \ -o {params.prefix} \ -sites_save \ -snp_weights \ -admix \ -threads {threads} \ > {log} 2>&1 """ |
94 95 96 97 | shell: """ bwa-mem2 index {input} > {log} 2>&1 """ |
117 118 119 120 121 122 123 124 125 | shell: """ bwa-mem2 mem -t {threads} \ -R \"{params.rg_string}\" \ {input.fa} \ {input.fastqs} 2> {log} | \ samtools sort -@ {threads} -o {output[0]} - samtools index {output[0]} """ |
134 135 136 137 138 | shell: """ bcftools query -f '%CHROM\\t%POS\\n' {input} | \ grep -v "ref" > {output} """ |
154 155 156 157 158 159 160 161 162 163 | shell: """ bcftools mpileup \ -f {input.ref} \ -R {input.targets} \ --redo-BAQ -a "FORMAT/AD,FORMAT/DP" \ -Ou {input.bams} 2> {log} | \ bcftools call -mG - -Ob --threads {threads} -o {output} \ >> {log} 2>&1 """ |
178 179 180 181 182 183 184 | shell: """ bcftools index -f {input.bcf1} bcftools index -f {input.bcf2} bcftools merge -m snps -Ob -o {output} -R {input.targets} \ --threads {threads} {input.bcf1} {input.bcf2} > {log} 2>&1 """ |
15 16 | script: "../scripts/btk_conta_extraction.py" |
31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | shell: """ tail -n +2 {input[1]} | cut -d ',' -f 1 > {params.tmp_kept_list} {params.blobtools_bin} filter \ --list {params.tmp_kept_list} \ --output {output[0]} {params.blobdir} {params.blobtools_bin} filter \ --list {params.tmp_kept_list} \ --invert \ --output {output[1]} {params.blobdir} rm {params.tmp_kept_list} """ |
61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | shell: """ seqkit grep -f <(tail -n +2 {input.kept} | cut -d ',' -f 1) {input[0]} \ | seqkit replace -is -p "^N+|N+$" -r "" > {input.fa}_tmp seqkit fx2tab -n -i --gc --length -B N {input.fa}_tmp \ | awk '($2 >= 1000 && $4 < 90) {{print $1}}' > {input.fa}_filt_list seqkit grep -f {input.fa}_filt_list {input.fa}_tmp > {input.fa}_tmp2 seqkit sort --by-length -2 --reverse {input.fa}_tmp2 \ | seqkit replace -p .+ -r "{params.scaff_prefix}_{{nr}}" --nr-width {params.nr_width} \ | gzip -c > {output} rm {input.fa}_tmp* {input.fa}_filt_list """ |
37 38 39 40 41 42 43 | shell: """ wget {params.ftp_0} -O {output[0]} wget {params.ftp_1} -O {output[1]} wget {params.ftp_2} -O {output[2]} wget {params.ftp_3} -O {output[3]} """ |
25 26 27 28 | shell: """ cp {input} {output} """ |
41 42 43 44 45 46 | shell: """ agat_sp_manage_IDs.pl --gff {input} \ --ensembl --prefix {params.prefix} \ -o {output} > {log} 2>&1 """ |
57 58 59 60 61 | shell: """ agat_convert_sp_gxf2gxf.pl -g {input} -gvi 3 -gvo 3 \ -o {output} > {log} 2>&1 """ |
74 75 76 77 78 79 | shell: """ gff3sort.pl {input} > {params.tmp} bgzip {params.tmp} tabix -p gff {output} """ |
86 87 | shell: "cat {input} | gzip -c > {output}" |
94 95 | shell: "cp {input} {output}" |
19 20 21 22 23 24 25 26 | shell: """ ls {input} > {params.files} kmc -k{params.k} -t{threads} -m{params.mem} \ -ci1 -cs10000 @{params.files} \ {params.db} /tmp/ \ > {log} 2>&1 """ |
42 43 44 45 46 47 48 | shell: """ kmc_tools -t{threads} transform \ {params.db} histogram \ {output} -cx10000 \ > {log} 2>&1 """ |
65 66 67 68 69 70 71 | shell: """ /opt/genomescope2.0/genomescope.R -i {input} \ -o {params.outdir} -n {params.name_prefix} \ -k {params.k} -p 2 \ > {log} 2>&1 """ |
16 17 18 19 20 21 22 23 | shell: """ kat comp -t {threads} \ -o {params.outprefix} \ '{input[0]} {input[1]}' \ {input[2]} \ > {log} 2>&1 """ |
36 37 38 39 40 41 42 43 | shell: """ kat plot spectra-cn \ -o {output} \ -t {params.title} \ {input} \ > {log} 2>&1 """ |
11 12 13 14 | shell: """ bwa-mem2 index {input} > {log} 2>&1 """ |
32 33 34 35 36 37 38 | shell: """ bwa-mem2 mem -t {threads} {input.fa} \ {input.fastqs} 2> {log} | \ samtools sort -@ {threads} -o {output[0]} - samtools index {output[0]} """ |
49 50 51 52 | shell: """ samtools stats -@ {threads} {input} > {output[0]} """ |
67 68 69 70 71 72 73 74 75 76 | shell: """ mosdepth \ -t {threads} \ --d4 \ --mapq {params.min_mapq} \ {params.prefix} \ {input} \ > {log} 2>&1 """ |
12 13 14 15 16 17 | shell: """ zcat {input[0]} | \ bedtools maskfasta -fi /dev/stdin -bed {input[1]} -fo /dev/stdout | \ gzip -c > {output} """ |
30 31 32 33 34 | shell: """ seqkit rmdup -s -D {params} -i \ -o {output} {input} """ |
50 51 52 53 54 55 | shell: """ seqkit replace -p .+ \ -r "{params.scaff_prefix}_s{{nr}}" --nr-width {params.nr_width} \ -o {output} {input} """ |
64 65 66 67 68 69 70 71 72 73 | shell: """ seqkit head -n 14 {input} | \ seqkit replace -p "(.+)" -r "\$1 [location=chromosome] [chromosome=LG{{nr}}]" \ --nr-width 2 \ -o {output} seqkit fx2tab {input} | tail -n +15 | seqkit tab2fx | gzip -c >> {output} """ |
84 85 86 87 88 89 90 | shell: """ paste \ <(seqkit seq --name --only-id {input[0]}) \ <(seqkit seq --name --only-id {input[1]}) \ > {output} """ |
101 102 103 104 105 106 | shell: """ awk -F'\\t' 'NR==FNR{{a[$1]=$2; next}}{{id=$1; if(id in a) gsub(id,a[id])}} 1' \ {input[0]} <(zcat {input[1]}) | \ gzip -c > {output} """ |
115 116 117 118 119 | shell: """ cp {input[0]} {output[0]} cp {input[1]} {output[1]} """ |
10 11 12 13 14 15 | shell: """ nubeam-dedup -i1 {input.fq1} -i2 {input.fq2} \ -o1 {output[0]} -o2 {output[1]} \ > {log} 2>&1 """ |
32 33 34 35 36 37 38 | shell: """ /opt/proc10xG/process_10xReads.py \ -o {params.out_prefix} \ -1 {input[0]} -2 {input[1]} \ > {log} 2>&1 """ |
48 49 | script: "../scripts/filter_barcodes.R" |
68 69 70 71 72 73 74 75 76 77 78 79 80 | shell: """ fastp -i {input[0]} -I {input[1]} \ -o {output[0]} -O {output[1]} \ --disable_length_filtering \ --correction \ --trim_poly_g \ --overrepresentation_analysis \ --json {params.report}.json \ --html {params.report}.html \ -w {threads} \ > {log} 2>&1 """ |
96 97 98 99 100 101 102 103 | shell: """ /opt/proc10xG/filter_10xReads.py \ -L {input.barcodes} \ -1 {input[0]} -2 {input[1]} \ -o {params.out_prefix} \ > {log} 2>&1 """ |
118 119 120 121 122 123 124 | shell: """ /opt/proc10xG/regen_10xReads.py \ -1 {input[0]} -2 {input[1]} \ -o {params.out_prefix} \ > {log} 2>&1 """ |
13 14 15 16 17 18 | shell: """ zcat {input.fa} > {params.tmp_fa} longranger mkref {params.tmp_fa} > {log} 2>&1 mv refdata-{wildcards.sample}_v1.pseudohap {output[0]} """ |
38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | shell: """ longranger align \ --id={params.run_id} \ --fastqs={params.input_dir} \ --sample={params.sample} \ --reference={input[2]} \ --localcores={threads} \ --localmem={params.mem} \ > {log} 2>&1 rm -r {output[0]} \ && cp -al {params.run_id} {output[0]} \ && rm -r {params.run_id} """ |
67 68 | shell: "samtools sort -@ {threads} -n -O BAM -o {output} {input} > {log} 2>&1" |
80 81 82 83 84 | shell: """ cd {params.workdir} ngscstat {params.input} 2> ../../../{log} """ |
93 94 95 96 | shell: """ calcuts {input[0]} > {output} 2> {log} """ |
104 105 106 107 108 | shell: "/opt/purge_dups/scripts/hist_plot.py " "-c {input[0]} " "{input[1]} " "{output}" |
117 118 | shell: "split_fa {input} > {output} 2> {log}" |
131 132 133 134 | shell: "(minimap2 -t {threads} " "-xasm5 -DP {input} {input} " "| gzip -c - > {output}) 2> {log}" |
148 149 150 151 | shell: "purge_dups -2 -M{params.M} -E{params.E} -T {input.cutoffs} " "-c {input.basecov} {input.selfmap} > {output} " "2> {log}" |
163 164 165 166 167 168 | shell: """ get_seqs {input.bed} {input.fa} \ -p {params.prefix} gzip {params.prefix}.*.fa """ |
175 176 | shell: "cp {input} {output}" |
183 184 | shell: "cp {input} {output}" |
14 15 | shell: "zcat {input} > {output}" |
28 29 30 31 32 33 | shell: """ export LC_ALL=C BuildDatabase -name {params.db_name} {input} \ > {log} 2>&1 """ |
49 50 51 52 53 54 | shell: """ export LC_ALL=C RepeatModeler -database {params.db_name} -LTRStruct -pa {params.pa} \ > {log} 2>&1 """ |
70 71 72 73 74 75 76 77 78 79 80 81 | shell: """ cat {input} > {params.tmp_merge} cd-hit-est -aS 0.8 -c 0.8 -g 1 -G 0 -A 80 -M 10000 \ -T {threads} \ -i {params.tmp_merge} \ -o {output} \ > {log} 2>&1 rm {params.tmp_merge} """ |
98 99 100 101 102 103 104 105 106 | shell: """ export LC_ALL=C RepeatMasker -dir {params.out_dir} \ -lib {input.families} \ -xsmall -gff -pa {params.pa} \ {input.fa} \ > {log} 2>&1 """ |
14 15 16 17 18 19 | shell: """ zcat {input} | \ assembly_stats /dev/stdin \ > {output} """ |
34 35 | script: "../scripts/merge_stats.py" |
45 46 47 48 | shell: """ d4tools stat -s hist {input} > {output} """ |
58 59 | script: "../scripts/plot_coverage_hist.R" |
18 19 20 21 22 23 24 25 26 27 28 29 30 31 | shell: """ cd tmp [ ! -e {params.run_id}/{params.run_id}.mri.tgz ] && rm -r {params.run_id} supernova run \ --id={params.run_id} \ --fastqs=../{params.input_dir} \ --sample={params.sample} \ --maxreads='all' \ --localcores={threads} \ --localmem={params.mem} \ --accept-extreme-coverage \ > ../{log} 2>&1; """ |
50 51 52 53 54 55 56 57 58 | shell: """ supernova mkoutput \ --style={params.style} \ --asmdir={params.asm_dir} \ --outprefix={params.outprefix} \ --headers=full \ > {log} 2>&1 """ |
78 79 80 81 82 83 | shell: """ tar -cf - -C {params.input_dir}/outs assembly | zstdmt -T{threads} > {params.tmp_archive} 2> {log} && \ rm -r {params.input_dir}/outs/assembly && \ cp -r {params.input_dir} {params.output_dir} && rm -r {params.input_dir} """ |
91 92 | shell: "bash workflow/scripts/collect_assembly_stats.sh" |
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | import json import pandas as pd import numpy as np files = { 'id': 'identifiers.json', 'gc': 'gc.json', 'cov': 'GM_cov.json', 'length': 'length.json', 'Ncount': 'ncount.json', 'superkingdom': 'bestsumorder_superkingdom.json', 'phylum': 'bestsumorder_phylum.json' } blobdir = snakemake.params.blobdir df = pd.DataFrame() for var, file in files.items(): with open(f'{blobdir}/{file}') as fr: data = json.load(fr) if data['keys'] == []: data = data['values'] else: data = [data['keys'][x] for x in data['values']] df[var] = data with open(f'{blobdir}/bestsumorder_positions.json') as fr: pos = json.load(fr) df['besthit_length'] = [(x[0][2] - x[0][1] + 1) if x!=[] else np.nan for x in pos['values']] df['taxid'] = [str(x[0][0]) if x!=[] else np.nan for x in pos['values']] df['besthit_perc'] = df.besthit_length/df.length df['N_perc'] = df.Ncount/df.length with open(snakemake.params.mollusca_taxids) as fr: mollusca_taxids = set(int(x.strip()) for x in fr.readlines()) taxids = [[y[0] for y in x] if x!=[] else [] for x in pos['values']] df['any_Mollusca_hit'] = [(len(set(x) & mollusca_taxids) > 0) for x in taxids] df_bacteria = df[(df.superkingdom=='Bacteria')] df_viruses = df[(df.superkingdom=='Viruses') & (df.besthit_perc > 0.1)] df_eukaryota = df[ (df.superkingdom=='Eukaryota') & (~df.phylum.isin(['Mollusca', 'no-hit'])) & (df.any_Mollusca_hit == False) & (df.besthit_perc > 0.1) ] conta_ids = df_bacteria.id.tolist() \ + df_viruses.id.tolist() \ + df_eukaryota.id.tolist() df_kept = df[~df.id.isin(conta_ids)] df_kept.to_csv(snakemake.output.kept, index=False) df_bacteria.to_csv(snakemake.output.bact, index=False) df_viruses.to_csv(snakemake.output.virus, index=False) df_eukaryota.to_csv(snakemake.output.euka, index=False) |
3 4 5 6 7 8 9 10 11 | echo "species,version,$(head -n1 results/supernova_assemblies/gallo_v1/outs/summary.csv)" \ > results/supernova_assemblies/supernova_assemblies_stats.csv for S in 'gallo' 'edu' 'tros'; do for V in 'v1' 'v2'; do echo "${S},${V},$(tail -n +2 results/supernova_assemblies/${S}_${V}/outs/summary.csv)" \ >> results/supernova_assemblies/supernova_assemblies_stats.csv done done |
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | options(warn = -1) # to remove some ggplot warnings library(ggplot2) library(stats) library(scales) barcode_input <- snakemake@input[["in_barcodes"]] barcode_output <- snakemake@output[["out_barcodes"]] fig_out <- snakemake@output[["figure"]] X <- read.table(barcode_input, header = F) H <- hist(X$V2, breaks = seq(0, max(X$V2), 1), plot = F) D <- data.frame(cbind(H$breaks[2:(max(X$V2)+1)], H$counts)) NZ <- D[!(D$X2 %in% 0), ] TOT <- sum((NZ$X1)*(NZ$X2)) count_empirical <- splinefun(x = NZ$X1, y = NZ$X2) MIN <- floor(optimize(function(x) count_empirical(x), c(1, 50))$minimum) MAX <- round((optimize(function(x) count_empirical(x), c(MIN, 500), maximum = T))$maximum) LOW_REM <- sum((NZ[NZ$X1 < MIN, ]$X1)*(NZ[NZ$X1 < MIN, ]$X2)) UP_REM <- sum((NZ[NZ$X1 > 10*MAX, ]$X1)*(NZ[NZ$X1 > 10*MAX, ]$X2)) NKEPT <- TOT - LOW_REM - UP_REM PKEPT <- NKEPT/TOT X_out <- X[X$V2 >= MIN & X$V2 <= 10*MAX, ] P <- ggplot(NZ, aes(x = X1, y = X2)) + xlab("Read pairs in bacode") + ylab("Count") + scale_x_log10(breaks = trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", math_format(10^.x))) + scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", math_format(10^.x))) + annotate("rect", xmin = MIN, xmax = 10*MAX, ymin = 0, ymax = +Inf, fill = 'green', alpha = 0.2) + annotate("rect", xmin = 0, xmax = MIN, ymin = 0, ymax = +Inf, fill = 'red', alpha = 0.2) + annotate("rect", xmin = 10*MAX, xmax = +Inf, ymin = 0, ymax = +Inf, fill='red', alpha = 0.2) + annotate("text", x = MAX, y = 5e4, label = "Maximum") + annotate("text", x = MAX, y = 3e4, label = MAX) + annotate("text", x = MAX, y = 1.7e5, label = "Mean") + annotate("text", x = MAX, y = 1e5, label = round(mean(X_out$V2), 1)) + annotate("text", x = 3, y = 100, label = "Removed low") + annotate("text", x = MAX, y = 100, label = "Retained") + annotate("text", x = 2000,y = 100, label = "Removed high") + annotate("text", x = 3, y = 55, label = LOW_REM) + annotate("text", x = MAX, y = 55, label = NKEPT) + annotate("text", x = 2000, y = 55, label = UP_REM) + annotate("text", x = 3, y = 30, label = round(LOW_REM/TOT, 3)) + annotate("text", x = MAX, y = 30, label = round(NKEPT/TOT, 3)) + annotate("text", x = 2000, y = 30, label = round(UP_REM/TOT, 3)) + geom_point() + geom_line(color = "blue") + theme_bw() ggsave(fig_out, P, width = 8, height = 4) writeLines(X_out$V1, barcode_output) |
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | import pandas as pd import json import re out_df = pd.DataFrame() for stat_file in snakemake.input: m = re.search('results/stats/(.+?)_(.+?).stats.json', stat_file) sample = m.group(1) version = m.group(2) with open(stat_file, 'r') as f: data = json.loads(f.read()) data['C'] = data.pop('Contig Stats') data['S'] = data.pop('Scaffold Stats') tmpdf = pd.json_normalize(data) tmpdf.insert(0, "asm", [f"{sample}_{version}"]) out_df = out_df.append(tmpdf) out_df.to_csv(snakemake.output[0], sep=',', index=False) |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | library(ggplot2) library(ggforce) library(glue) theme_set(theme_minimal(base_size = 12)) defaultW <- getOption("warn") options(warn = -1) # get params in_hist = snakemake@input[['hist']] out_pdf = snakemake@output[['pdf']] sample_name = paste0(snakemake@wildcards[['sample']], "_", snakemake@wildcards[['version']]) df = read.table(in_hist, col.names = c("bin", "count"))[-1,] df$bin_int = as.numeric(df$bin) df$bin_int[1001] = 1001 # gross mean m = sum(df$bin_int*df$count)/sum(df$count) # take care of too low coverage cases if (m >= 5){ max_bin = 2*m } else { max_bin = 5 } p = ggplot(df, aes(x = bin_int, y = count)) + geom_bar(stat = 'identity') + scale_x_continuous(expand = c(0,0)) + scale_y_continuous(expand = c(0,0)) + labs( x = "Coverage", y = "Count", title = glue("Histogram of coverage for {sample_name} (last bin is >=1000)") ) + theme( panel.grid.minor.x = element_blank(), panel.grid.major.x = element_blank(), plot.margin = margin(10, 25, 10, 25), axis.ticks.x = element_line(), axis.ticks.length.x = unit(5, "pt"), strip.background = element_rect(fill = "grey90", linetype = "blank") ) + facet_zoom(xy = (bin_int > 0 & bin_int <= max_bin), zoom.size = 1, horizontal = F) ggsave(out_pdf, p, width = 10, height = 8) options(warn = defaultW) |
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import pathlib import defopt import sys import pandas as pd def parse_busco(filename): """ Parse one short_summary BUSCO file """ asm = filename.name.split('.')[3] db = filename.name.split('.')[2] data = {'asm': asm, 'db': db} with open(filename, 'r') as fr: for line in fr: if "Complete and single-copy BUSCOs" in line: data['CS'] = int(line.split("\t")[1]) elif "Complete and duplicated BUSCOs" in line: data['CD'] = int(line.split("\t")[1]) elif "Fragmented BUSCOs" in line: data['F'] = int(line.split("\t")[1]) elif "Missing BUSCOs" in line: data['M'] = int(line.split("\t")[1]) data['C'] = data['CS'] + data['CD'] data['T'] = data['CS'] + data['CD'] + data['F'] + data['M'] return data def format_output(out, datasets): df = pd.DataFrame(datasets) df = df.melt(id_vars=['asm', 'db'], value_vars=['CS', 'CD', 'F', 'M', 'C', 'T'], var_name='cat', value_name='N') if out is None: df.to_csv(sys.stdout, sep='\t', index=False) else: df.to_csv(out, sep='\t', index=False) def main(files: list[pathlib.Path], out: pathlib.Path=None): """ Summarize a list of short summary BUSCO files into a tidy table. :param files: BUSCO short_summary files (space separated list) :param out: Summary output as tsv, stdout by default """ datasets = [] for busco_file in files: datasets.append(parse_busco(busco_file)) format_output(out, datasets) if __name__ == '__main__': defopt.run(main, strict_kwonly=False, short={'out': 'o'}) |
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Created: 1yr ago
Updated: 1yr ago
Maitainers:
public
URL:
https://github.com/alxsimon/assembly_10x
Name:
assembly_10x
Version:
v1.0
Downloaded:
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Copyright:
Public Domain
License:
None
Keywords:
GFF
JSON
Data
Genome assembly
Sequence assembly report
angsd
augustus
tabix
BCFtools
BEDTools
BUSCO
Bwa-mem2
cd-hit
fastp
GLUE
HISAT2
KAT
KMC
MANTIS
Minimap2
mosdepth
Nubeam-dedup
Pandas
RepeatMasker
RepeatModeler
SAMtools
seqkit
Snakemake
Supernova
ggforce
ggplot2
scales
defopt
numpy
Trim_Galore
Sequence assembly
- Future updates
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