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Segmentation fault error?

Issue #83 resolved
zhenjian lin created an issue

Could you please help me for this error when running metabat2? my node mem is 256GB

Executing: 'jgi_summarize_bam_contig_depths --outputDepth scaffolds.fasta.depth.txt --percentIdentity 97 --minContigLength 1000 --minContigDepth 1.0 --referenceFasta bbm_spades_merge/scaffolds.fasta aln.sorted.bam.bam' at Fri Nov 1 10:49:09 MDT 2019
Output depth matrix to scaffolds.fasta.depth.txt
Minimum percent identity for a mapped read: 0.97
minContigLength: 1000
minContigDepth: 1
Reference fasta file bbm_spades_merge/scaffolds.fasta
jgi_summarize_bam_contig_depths GIT-NOTFOUND 2019-10-30T14:39:24
Output matrix to scaffolds.fasta.depth.txt
Reading reference fasta file: bbm_spades_merge/scaffolds.fasta
... 462447 sequences
0: Opening bam: aln.sorted.bam.bam
Processing bam files
WARNING: your aligner reports an incorrect NM field. You should run samtools calmd! nm < ins + del: cmatch=0 nm=0 ( insert=0 + del=48 + mismatch=0 == 48) HWI-D00550:379:C9TRMANXX:7:1101:11493:76772 1:N:0:GCCAAT
Thread 0 finished: aln.sorted.bam.bam with 276884090 reads and 246175557 readsWellMapped
Creating depth matrix file: scaffolds.fasta.depth.txt
Closing most bam files
Closing last bam file
Finished
Finished jgi_summarize_bam_contig_depths at Fri Nov 1 10:58:28 MDT 2019
Creating depth file for metabat at Fri Nov 1 10:58:28 MDT 2019
Executing: 'metabat2 --inFile bbm_spades_merge/scaffolds.fasta --outFile scaffolds.fasta.metabat-bins-20191101_105828/bin --abdFile scaffolds.fasta.depth.txt' at Fri Nov 1 10:58:28 MDT 2019
MetaBAT 2 (GIT-NOTFOUND) using minContig 2500, minCV 1.0, minCVSum 1.0, maxP 95%, minS 60, and maxEdges 200.
17 bins (352959735 bases in total) formed.
/uufs/chpc.utah.edu/sys/installdir/metabat/10302019/bin/runMetaBat.sh: line 125: 8794 Segmentation fault $MB $metabatopts --inFile $assembly --outFile $outname --abdFile ${depth}

Comments (4)

  1. Rob Egan

    Hello, Sorry you experienced this.

    That segfault is not very informative as it looks like it happened near the end after all the bins were output. Were all 17 output bins produced?

    If you can get a core dump and a stack trace that would be helpful. Additionally executing with the -v option might help too.

    Lastly, I just pushed some code which might fix the problem and if not would help to diagnose what broke, again if you execute with the -v option.

    ‌

  2. zhenjian lin reporter

    would this help to figure out what is wrong with my bamfile?

    I got 0 bin

    The following have been reloaded with a version change:

    1. intel/2018.1.163 => intel/2019.1.144

    Executing: 'jgi_summarize_bam_contig_depths --outputDepth scaffolds.fasta.depth.txt --percentIdentity 97 --minContigLength 1000 --minContigDepth 1.0 --referenceFasta bbm_spades_merge/scaffolds.fasta test3.bam' at Fri Nov 1 21:45:16 MDT 2019
    Output depth matrix to scaffolds.fasta.depth.txt
    Minimum percent identity for a mapped read: 0.97
    minContigLength: 1000
    minContigDepth: 1
    Reference fasta file bbm_spades_merge/scaffolds.fasta
    jgi_summarize_bam_contig_depths GIT-NOTFOUND 2019-10-30T14:39:24
    Output matrix to scaffolds.fasta.depth.txt
    Reading reference fasta file: bbm_spades_merge/scaffolds.fasta
    ... 462447 sequences
    0: Opening bam: test3.bam
    [W::bam_hdr_read] EOF marker is absent. The input is probably truncated
    Processing bam files
    Thread 0 finished: test3.bam with 272519018 reads and 248544724 readsWellMapped
    Creating depth matrix file: scaffolds.fasta.depth.txt
    Closing most bam files
    Closing last bam file
    Finished
    Finished jgi_summarize_bam_contig_depths at Fri Nov 1 21:50:09 MDT 2019
    Creating depth file for metabat at Fri Nov 1 21:50:09 MDT 2019
    Executing: 'metabat2 -v --inFile bbm_spades_merge/scaffolds.fasta --outFile scaffolds.fasta.metabat-bins-v-20191101_215009/bin --abdFile scaffolds.fasta.depth.txt' at Fri Nov 1 21:50:09 MDT 2019
    MetaBAT 2 (GIT-NOTFOUND) using minContig 2500, minCV 1.0, minCVSum 1.0, maxP 95%, minS 60, and maxEdges 200.
    [00:00:00] Executing with 56 threads
    [00:00:00] Parsing abundance file
    [00:00:00] Parsing assembly file
    [00:00:01] Number of large contigs >= 2500 are 67845.
    [00:00:01] Reading abundance file
    [00:00:02] Finished reading 134492 contigs and 1 coverages from scaffolds.fasta.depth.txt
    [00:00:02] Number of target contigs: 67845 of large (>= 2500) and 66647 of small ones (>=1000 & <2500).
    [00:00:02] Start TNF calculation. nobs = 67845
    [00:00:02] Finished TNF calculation.
    [00:00:02] Attempt 0 of 10 to gen_tnf_graph_sample
    [00:00:03] Preparing TNF Graph Building [pTNF = 99.9; 2 / 2500 (P = 0.08%) round 1] ^M[00:00:03] Preparing TNF Graph Building [pTNF = 99.8; 143 / 2500 (P = 5.72%) round 2] ^M[00:00:04] Preparing TNF Graph Building [pTNF = 99.5; 683 / 2500 (P = 27.32%) round 3] ^M[00:00:04] Preparing TNF Graph Building [pTNF = 99.3; 966 / 2500 (P = 38.64%) round 4] ^M[00:00:04] Preparing TNF Graph Building [pTNF = 99.1; 1228 / 2500 (P = 49.12%) round 5] ^M[00:00:04] Preparing TNF Graph Building [pTNF = 98.8; 1562 / 2500 (P = 62.48%) round 6] ^M[00:00:04] Preparing TNF Graph Building [pTNF = 98.4; 1833 / 2500 (P = 73.32%) round 7] ^M[00:00:04] Preparing TNF Graph Building [pTNF = 98.0; 2032 / 2500 (P = 81.28%) round 8] ^M[00:00:04] Preparing TNF Graph Building [pTNF = 97.6; 2141 / 2500 (P = 85.64%) round 9] ^M[00:00:04] Preparing TNF Graph Building [pTNF = 97.1; 2231 / 2500 (P = 89.24%) round 10] ^M[00:00:04] Preparing TNF Graph Building [pTNF = 96.7; 2285 / 2500 (P = 91.40%) round 11] ^M[00:00:04] Preparing TNF Graph Building [pTNF = 96.3; 2325 / 2500 (P = 93.00%) round 12] ^M[00:00:04] Preparing TNF Graph Building [pTNF = 95.8; 2370 / 2500 (P = 94.80%) round 13] ^M[00:00:04]
    [00:00:04] Attempt 1 of 10 to gen_tnf_graph_sample
    [00:00:05] Preparing TNF Graph Building [pTNF = 99.9; 7 / 2500 (P = 0.28%) round 1] ^M[00:00:05] Preparing TNF Graph Building [pTNF = 99.8; 153 / 2500 (P = 6.12%) round 2] ^M[00:00:05] Preparing TNF Graph Building [pTNF = 99.7; 355 / 2500 (P = 14.20%) round 3] ^M[00:00:05] Preparing TNF Graph Building [pTNF = 99.6; 543 / 2500 (P = 21.72%) round 4] ^M[00:00:05] Preparing TNF Graph Building [pTNF = 99.3; 990 / 2500 (P = 39.60%) round 5] ^M[00:00:05] Preparing TNF Graph Building [pTNF = 99.0; 1367 / 2500 (P = 54.68%) round 6] ^M[00:00:05] Preparing TNF Graph Building [pTNF = 98.7; 1649 / 2500 (P = 65.96%) round 7] ^M[00:00:05] Preparing TNF Graph Building [pTNF = 98.3; 1898 / 2500 (P = 75.92%) round 8] ^M[00:00:05] Preparing TNF Graph Building [pTNF = 98.0; 2035 / 2500 (P = 81.40%) round 9] ^M[00:00:05] Preparing TNF Graph Building [pTNF = 97.7; 2133 / 2500 (P = 85.32%) round 10] ^M[00:00:05] Preparing TNF Graph Building [pTNF = 97.3; 2226 / 2500 (P = 89.04%) round 11] ^M[00:00:05] Preparing TNF Graph Building [pTNF = 97.0; 2265 / 2500 (P = 90.60%) round 12] ^M[00:00:05] Preparing TNF Graph Building [pTNF = 96.5; 2315 / 2500 (P = 92.60%) round 13] ^M[00:00:05] Preparing TNF Graph Building [pTNF = 96.2; 2343 / 2500 (P = 93.72%) round 14] ^M[00:00:05]
    [00:00:05] Attempt 2 of 10 to gen_tnf_graph_sample
    [00:00:06] Preparing TNF Graph Building [pTNF = 99.9; 6 / 2500 (P = 0.24%) round 1] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 99.8; 130 / 2500 (P = 5.20%) round 2] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 99.7; 323 / 2500 (P = 12.92%) round 3] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 99.6; 482 / 2500 (P = 19.28%) round 4] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 99.4; 798 / 2500 (P = 31.92%) round 5] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 99.3; 933 / 2500 (P = 37.32%) round 6] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 99.1; 1228 / 2500 (P = 49.12%) round 7] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 98.9; 1432 / 2500 (P = 57.28%) round 8] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 98.4; 1818 / 2500 (P = 72.72%) round 9] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 98.0; 2001 / 2500 (P = 80.04%) round 10] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 97.7; 2108 / 2500 (P = 84.32%) round 11] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 97.3; 2202 / 2500 (P = 88.08%) round 12] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 96.9; 2265 / 2500 (P = 90.60%) round 13] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 96.6; 2298 / 2500 (P = 91.92%) round 14] ^M[00:00:06] Preparing TNF Graph Building [pTNF = 96.1; 2346 / 2500 (P = 93.84%) round 15] ^M[00:00:06]
    [00:00:06] Attempt 3 of 10 to gen_tnf_graph_sample
    [00:00:07] Preparing TNF Graph Building [pTNF = 99.9; 7 / 2500 (P = 0.28%) round 1] ^M[00:00:07] Preparing TNF Graph Building [pTNF = 99.7; 316 / 2500 (P = 12.64%) round 2] ^M[00:00:07] Preparing TNF Graph Building [pTNF = 99.6; 491 / 2500 (P = 19.64%) round 3] ^M[00:00:07] Preparing TNF Graph Building [pTNF = 99.3; 925 / 2500 (P = 37.00%) round 4] ^M[00:00:07] Preparing TNF Graph Building [pTNF = 99.0; 1301 / 2500 (P = 52.04%) round 5] ^M[00:00:07] Preparing TNF Graph Building [pTNF = 98.5; 1747 / 2500 (P = 69.88%) round 6] ^M[00:00:07] Preparing TNF Graph Building [pTNF = 98.2; 1927 / 2500 (P = 77.08%) round 7] ^M[00:00:07] Preparing TNF Graph Building [pTNF = 97.7; 2126 / 2500 (P = 85.04%) round 8] ^M[00:00:07] Preparing TNF Graph Building [pTNF = 97.2; 2238 / 2500 (P = 89.52%) round 9] ^M[00:00:07] Preparing TNF Graph Building [pTNF = 96.9; 2285 / 2500 (P = 91.40%) round 10] ^M[00:00:07] Preparing TNF Graph Building [pTNF = 96.5; 2320 / 2500 (P = 92.80%) round 11] ^M[00:00:07] Preparing TNF Graph Building [pTNF = 96.0; 2362 / 2500 (P = 94.48%) round 12] ^M[00:00:07]
    [00:00:07] Attempt 4 of 10 to gen_tnf_graph_sample
    [00:00:08] Preparing TNF Graph Building [pTNF = 99.9; 3 / 2500 (P = 0.12%) round 1] ^M[00:00:08] Preparing TNF Graph Building [pTNF = 99.8; 137 / 2500 (P = 5.48%) round 2] ^M[00:00:08] Preparing TNF Graph Building [pTNF = 99.6; 524 / 2500 (P = 20.96%) round 3] ^M[00:00:08] Preparing TNF Graph Building [pTNF = 99.4; 855 / 2500 (P = 34.20%) round 4] ^M[00:00:08] Preparing TNF Graph Building [pTNF = 99.2; 1104 / 2500 (P = 44.16%) round 5] ^M[00:00:08] Preparing TNF Graph Building [pTNF = 98.9; 1449 / 2500 (P = 57.96%) round 6] ^M[00:00:08] Preparing TNF Graph Building [pTNF = 98.5; 1814 / 2500 (P = 72.56%) round 7] ^M[00:00:08] Preparing TNF Graph Building [pTNF = 98.0; 2064 / 2500 (P = 82.56%) round 8] ^M[00:00:08] Preparing TNF Graph Building [pTNF = 97.5; 2221 / 2500 (P = 88.84%) round 9] ^M[00:00:08] Preparing TNF Graph Building [pTNF = 97.1; 2295 / 2500 (P = 91.80%) round 10] ^M[00:00:08] Preparing TNF Graph Building [pTNF = 96.8; 2320 / 2500 (P = 92.80%) round 11] ^M[00:00:08] Preparing TNF Graph Building [pTNF = 96.3; 2363 / 2500 (P = 94.52%) round 12] ^M[00:00:08]
    [00:00:08] Attempt 5 of 10 to gen_tnf_graph_sample
    [00:00:09] Preparing TNF Graph Building [pTNF = 99.9; 8 / 2500 (P = 0.32%) round 1] ^M[00:00:09] Preparing TNF Graph Building [pTNF = 99.7; 333 / 2500 (P = 13.32%) round 2] ^M[00:00:09] Preparing TNF Graph Building [pTNF = 99.6; 507 / 2500 (P = 20.28%) round 3] ^M[00:00:09] Preparing TNF Graph Building [pTNF = 99.4; 802 / 2500 (P = 32.08%) round 4] ^M[00:00:09] Preparing TNF Graph Building [pTNF = 99.1; 1218 / 2500 (P = 48.72%) round 5] ^M[00:00:09] Preparing TNF Graph Building [pTNF = 98.8; 1542 / 2500 (P = 61.68%) round 6] ^M[00:00:09] Preparing TNF Graph Building [pTNF = 98.5; 1785 / 2500 (P = 71.40%) round 7] ^M[00:00:09] Preparing TNF Graph Building [pTNF = 98.1; 2009 / 2500 (P = 80.36%) round 8] ^M[00:00:09] Preparing TNF Graph Building [pTNF = 97.8; 2125 / 2500 (P = 85.00%) round 9] ^M[00:00:09] Preparing TNF Graph Building [pTNF = 97.5; 2197 / 2500 (P = 87.88%) round 10] ^M[00:00:09] Preparing TNF Graph Building [pTNF = 97.1; 2266 / 2500 (P = 90.64%) round 11] ^M[00:00:09] Preparing TNF Graph Building [pTNF = 96.6; 2322 / 2500 (P = 92.88%) round 12] ^M[00:00:09] Preparing TNF Graph Building [pTNF = 96.3; 2348 / 2500 (P = 93.92%) round 13] ^M[00:00:09] Preparing TNF Graph Building [pTNF = 96.0; 2367 / 2500 (P = 94.68%) round 14] ^M[00:00:09]
    [00:00:09] Attempt 6 of 10 to gen_tnf_graph_sample
    [00:00:10] Preparing TNF Graph Building [pTNF = 99.9; 5 / 2500 (P = 0.20%) round 1] ^M[00:00:10] Preparing TNF Graph Building [pTNF = 99.8; 158 / 2500 (P = 6.32%) round 2] ^M[00:00:10] Preparing TNF Graph Building [pTNF = 99.5; 649 / 2500 (P = 25.96%) round 3] ^M[00:00:10] Preparing TNF Graph Building [pTNF = 99.3; 948 / 2500 (P = 37.92%) round 4] ^M[00:00:10] Preparing TNF Graph Building [pTNF = 99.1; 1189 / 2500 (P = 47.56%) round 5] ^M[00:00:10] Preparing TNF Graph Building [pTNF = 98.9; 1413 / 2500 (P = 56.52%) round 6] ^M[00:00:10] Preparing TNF Graph Building [pTNF = 98.6; 1684 / 2500 (P = 67.36%) round 7] ^M[00:00:10] Preparing TNF Graph Building [pTNF = 98.1; 1978 / 2500 (P = 79.12%) round 8] ^M[00:00:10] Preparing TNF Graph Building [pTNF = 97.7; 2119 / 2500 (P = 84.76%) round 9] ^M[00:00:10] Preparing TNF Graph Building [pTNF = 97.3; 2211 / 2500 (P = 88.44%) round 10] ^M[00:00:10] Preparing TNF Graph Building [pTNF = 96.8; 2295 / 2500 (P = 91.80%) round 11] ^M[00:00:10] Preparing TNF Graph Building [pTNF = 96.5; 2322 / 2500 (P = 92.88%) round 12] ^M[00:00:10] Preparing TNF Graph Building [pTNF = 96.0; 2353 / 2500 (P = 94.12%) round 13] ^M[00:00:10]
    [00:00:10] Attempt 7 of 10 to gen_tnf_graph_sample
    [00:00:11] Preparing TNF Graph Building [pTNF = 99.9; 8 / 2500 (P = 0.32%) round 1] ^M[00:00:11] Preparing TNF Graph Building [pTNF = 99.8; 150 / 2500 (P = 6.00%) round 2] ^M[00:00:11] Preparing TNF Graph Building [pTNF = 99.6; 478 / 2500 (P = 19.12%) round 3] ^M[00:00:11] Preparing TNF Graph Building [pTNF = 99.3; 902 / 2500 (P = 36.08%) round 4] ^M[00:00:11] Preparing TNF Graph Building [pTNF = 99.1; 1190 / 2500 (P = 47.60%) round 5] ^M[00:00:11] Preparing TNF Graph Building [pTNF = 98.8; 1517 / 2500 (P = 60.68%) round 6] ^M[00:00:11] Preparing TNF Graph Building [pTNF = 98.5; 1763 / 2500 (P = 70.52%) round 7] ^M[00:00:11] Preparing TNF Graph Building [pTNF = 98.0; 2028 / 2500 (P = 81.12%) round 8] ^M[00:00:11] Preparing TNF Graph Building [pTNF = 97.5; 2187 / 2500 (P = 87.48%) round 9] ^M[00:00:11] Preparing TNF Graph Building [pTNF = 97.2; 2240 / 2500 (P = 89.60%) round 10] ^M[00:00:11] Preparing TNF Graph Building [pTNF = 96.7; 2325 / 2500 (P = 93.00%) round 11] ^M[00:00:11] Preparing TNF Graph Building [pTNF = 96.3; 2360 / 2500 (P = 94.40%) round 12] ^M[00:00:11]
    [00:00:11] Attempt 8 of 10 to gen_tnf_graph_sample
    [00:00:12] Preparing TNF Graph Building [pTNF = 99.9; 9 / 2500 (P = 0.36%) round 1] ^M[00:00:12] Preparing TNF Graph Building [pTNF = 99.8; 140 / 2500 (P = 5.60%) round 2] ^M[00:00:12] Preparing TNF Graph Building [pTNF = 99.5; 633 / 2500 (P = 25.32%) round 3] ^M[00:00:12] Preparing TNF Graph Building [pTNF = 99.2; 1097 / 2500 (P = 43.88%) round 4] ^M[00:00:12] Preparing TNF Graph Building [pTNF = 98.9; 1435 / 2500 (P = 57.40%) round 5] ^M[00:00:12] Preparing TNF Graph Building [pTNF = 98.5; 1738 / 2500 (P = 69.52%) round 6] ^M[00:00:12] Preparing TNF Graph Building [pTNF = 98.0; 1998 / 2500 (P = 79.92%) round 7] ^M[00:00:12] Preparing TNF Graph Building [pTNF = 97.6; 2139 / 2500 (P = 85.56%) round 8] ^M[00:00:12] Preparing TNF Graph Building [pTNF = 97.2; 2240 / 2500 (P = 89.60%) round 9] ^M[00:00:12] Preparing TNF Graph Building [pTNF = 96.9; 2290 / 2500 (P = 91.60%) round 10] ^M[00:00:12] Preparing TNF Graph Building [pTNF = 96.5; 2328 / 2500 (P = 93.12%) round 11] ^M[00:00:12] Preparing TNF Graph Building [pTNF = 96.2; 2348 / 2500 (P = 93.92%) round 12] ^M[00:00:12] Preparing TNF Graph Building [pTNF = 95.9; 2367 / 2500 (P = 94.68%) round 13] ^M[00:00:12]
    [00:00:12] Attempt 9 of 10 to gen_tnf_graph_sample
    [00:00:13] Preparing TNF Graph Building [pTNF = 99.9; 5 / 2500 (P = 0.20%) round 1] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 99.7; 296 / 2500 (P = 11.84%) round 2] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 99.6; 465 / 2500 (P = 18.60%) round 3] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 99.5; 624 / 2500 (P = 24.96%) round 4] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 99.3; 913 / 2500 (P = 36.52%) round 5] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 99.1; 1186 / 2500 (P = 47.44%) round 6] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 99.0; 1316 / 2500 (P = 52.64%) round 7] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 98.6; 1688 / 2500 (P = 67.52%) round 8] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 98.3; 1892 / 2500 (P = 75.68%) round 9] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 98.0; 2033 / 2500 (P = 81.32%) round 10] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 97.7; 2132 / 2500 (P = 85.28%) round 11] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 97.2; 2235 / 2500 (P = 89.40%) round 12] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 96.8; 2289 / 2500 (P = 91.56%) round 13] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 96.3; 2344 / 2500 (P = 93.76%) round 14] ^M[00:00:13] Preparing TNF Graph Building [pTNF = 95.9; 2372 / 2500 (P = 94.88%) round 15] ^M[00:00:13]
    [00:00:13] Finished Preparing TNF Graph Building [pTNF = 95.80]
    [00:00:14] Building TNF Graph 3.9% (2640 of 67845), ETA 0:00:20 [-60.4Gb / 251.8Gb] ^M[00:00:14] Building TNF Graph 7.8% (5280 of 67845), ETA 0:00:14 [-60.4Gb / 251.8Gb] ^M[00:00:14] Building TNF Graph 11.3% (7700 of 67845), ETA 0:00:12 [-60.3Gb / 251.8Gb] ^M[00:00:15] Building TNF Graph 14.9% (10120 of 67845), ETA 0:00:11 [-60.3Gb / 251.8Gb] ^M[00:00:15] Building TNF Graph 18.6% (12628 of 67845), ETA 0:00:10 [-60.3Gb / 251.8Gb] ^M[00:00:16] Building TNF Graph 22.3% (15136 of 67845), ETA 0:00:10 [-60.3Gb / 251.8Gb] ^M[00:00:16] Building TNF Graph 25.5% (17292 of 67845), ETA 0:00:09 [-60.3Gb / 251.8Gb] ^M[00:00:16] Building TNF Graph 29.2% (19844 of 67845), ETA 0:00:09 [-60.3Gb / 251.8Gb] ^M[00:00:17] Building TNF Graph 32.8% (22264 of 67845), ETA 0:00:08 [-60.3Gb / 251.8Gb] ^M[00:00:17] Building TNF Graph 36.4% (24684 of 67845), ETA 0:00:08 [-60.3Gb / 251.8Gb] ^M[00:00:18] Building TNF Graph 40.3% (27368 of 67845), ETA 0:00:07 [-60.3Gb / 251.8Gb] ^M[00:00:18] Building TNF Graph 44.1% (29920 of 67845), ETA 0:00:06 [-60.3Gb / 251.8Gb] ^M[00:00:18] Building TNF Graph 47.9% (32472 of 67845), ETA 0:00:06 [-60.3Gb / 251.8Gb] ^M[00:00:19] Building TNF Graph 51.0% (34584 of 67845), ETA 0:00:06 [-60.3Gb / 251.8Gb] ^M[00:00:19] Building TNF Graph 55.3% (37488 of 67845), ETA 0:00:05 [-60.3Gb / 251.8Gb] ^M[00:00:19] Building TNF Graph 58.8% (39908 of 67845), ETA 0:00:05 [-60.3Gb / 251.8Gb] ^M[00:00:20] Building TNF Graph 62.5% (42372 of 67845), ETA 0:00:04 [-60.3Gb / 251.8Gb] ^M[00:00:20] Building TNF Graph 66.0% (44748 of 67845), ETA 0:00:04 [-60.3Gb / 251.8Gb] ^M[00:00:21] Building TNF Graph 69.7% (47256 of 67845), ETA 0:00:03 [-60.3Gb / 251.8Gb] ^M[00:00:21] Building TNF Graph 73.0% (49500 of 67845), ETA 0:00:03 [-60.3Gb / 251.8Gb] ^M[00:00:21] Building TNF Graph 76.3% (51788 of 67845), ETA 0:00:03 [-60.3Gb / 251.8Gb] ^M[00:00:22] Building TNF Graph 80.0% (54252 of 67845), ETA 0:00:02 [-60.3Gb / 251.8Gb] ^M[00:00:22] Building TNF Graph 83.7% (56804 of 67845), ETA 0:00:02 [-60.3Gb / 251.8Gb] ^M[00:00:22] Building TNF Graph 87.4% (59268 of 67845), ETA 0:00:01 [-60.3Gb / 251.8Gb] ^M[00:00:23] Building TNF Graph 91.1% (61820 of 67845), ETA 0:00:01 [-60.3Gb / 251.8Gb] ^M[00:00:23] Building TNF Graph 94.9% (64372 of 67845), ETA 0:00:01 [-60.3Gb / 251.8Gb] ^M[00:00:24] Building TNF Graph 98.5% (66836 of 67845), ETA 0:00:00 [-60.3Gb / 251.8Gb] ^M[00:00:24] Finished Building TNF Graph (1481684 edges) [-60.2Gb / 251.8Gb]
    [00:00:25] Building SCR Graph and Binning (6446 vertices and 43640 edges) [P = 9.50%; -60.3Gb / 251.8Gb] ^M[00:00:25] Building SCR Graph and Binning (12891 vertices and 71134 edges) [P = 19.00%; -60.3Gb / 251.8Gb] ^M[00:00:25] Building SCR Graph and Binning (19336 vertices and 87990 edges) [P = 28.50%; -60.3Gb / 251.8Gb] ^M[00:00:25] Building SCR Graph and Binning (25783 vertices and 102657 edges) [P = 38.00%; -60.3Gb / 251.8Gb] ^M[00:00:26] Building SCR Graph and Binning (32227 vertices and 117644 edges) [P = 47.50%; -60.3Gb / 251.8Gb] ^M[00:00:26] Building SCR Graph and Binning (38672 vertices and 134579 edges) [P = 57.00%; -60.3Gb / 251.8Gb] ^M[00:00:26] Building SCR Graph and Binning (45117 vertices and 157742 edges) [P = 66.50%; -60.3Gb / 251.8Gb] ^M[00:00:27] Building SCR Graph and Binning (46408 vertices and 164498 edges) [P = 76.00%; -60.3Gb / 251.8Gb]
    [00:00:27] Outputting bins
    [00:00:27] 76.09% (354220128 bases) of large (>=2500) and 0.00% (0 bases) of small (<2500) contigs were binned.
    16 bins (354220128 bases in total) formed.
    /uufs/chpc.utah.edu/sys/installdir/metabat/10302019/bin/runMetaBat.sh: line 125: 67309 Segmentation fault $MB $metabatopts --inFile $assembly --outFile $outname --abdFile ${depth}

  3. Rob Egan

    Hi. So there are two things going on here.

    1. The bam file is incomplete.

    [W::bam_hdr_read] EOF marker is absent. The input is probably truncated

    This may be okay if all the records are present, but it means that it wasn’t properly terminated, likely because you built it with pipes or something. I have no insight as to what you did to create it. Please read the samtools documentation.

    You can inspect the bam file to count the # of reads to see if all the reads are present. Alternatively you can inspect the coverage file ‘scaffolds.fasta.depth.txt' to see if the coverage you have is around what you expect to see for the largest contigs.

    ‌

    2. Metabat segfaults at the very end.

    16 bins (354220128 bases in total) formed.
    /uufs/chpc.utah.edu/sys/installdir/metabat/10302019/bin/runMetaBat.sh: line 125: 67309 Segmentation fault $MB $metabatopts --inFile $assembly --outFile $outname --abdFile ${depth}

    ‌

    ‌

    I cannot replicate the segmentation fault, and would need a stack trace to further diagnose it. It looks like it is happening in the final lines of code while writing the output bins.

    Did the 16 bins get written?

    I just made a push that may fix it or at least point in the right direction of a fix: 6e0038d

    Since this is quick to run, you could also try to run with the environmental variable OMP_NUM_THREADS=1

    ‌

    -Rob

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