Overview

When you publish manuscripts based on data generated at our facility, we would greatly appreciate an acknowledgement of our efforts. Please cite our facility as follows (for example):

Basic processing of the raw data were performed by the University of Illinois at Chicago Research Informatics Core (UICRIC).

We adhere to a general policy for acknowledgements and authorship as established by the Association for Biomolecular Resource Facilities (ABRF) , and we support the following statement from the ABRF.

The existence of core facilities depends in part on proper acknowledgment in publications. This is an important metric of the value of most core facilities. Proper acknowledgment of core facilities enables them to obtain financial and other support so that they may continue to provide their essential services in the best ways possible. It also helps core personnel to advance in their careers, adding to the overall health of the core facility.

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File Description Type
taxa_raw_counts.xlsx Excel spreadsheet of taxonomic summaries from phylum to species level - raw sequence counts result
taxa_raw_counts.zip ZIP archive of taxonomic summaries from phylum to species level - raw sequence counts result
kegg_raw_counts.xlsx Excel spreadsheet of functional gene (KEGG ortholog) summaries - raw sequence counts result
kegg_raw_counts.zip ZIP archive of functional gene (KEGG ortholog) summaries - raw sequence counts result
metagenomic_assembly.zip ZIP compressed FASTA file of all contigs in the assembly result
contig_counts.txt Raw read counts for assembled contigs result
orf_counts.txt Raw read counts for predicted ORFs result
contig_data.txt Details and normalized read counts (RPKM) of the contigs result
contig_annotations.txt Gene annotations of predicted ORFs, as TSV file result
annotation_results.zip ZIP file of annotation results as and GFF format with FASTA files of nucleotide and amino acid sequences for each ORF result
bins_data.txt Details the metagenomic bins result

Sample list
Sample OriginalID
SRR22405441 SRR22405441
SRR22405442 SRR22405442
SRR22405445 SRR22405445
SRR22405453 SRR22405453
SRR22405460 SRR22405460
SRR22405461 SRR22405461
SRR22405467 SRR22405467
SRR22405532 SRR22405532
SRR22405538 SRR22405538
SRR22405544 SRR22405544

Method: cutadaptMartin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, 17(1):10-12. doi:https://doi.org/10.14806/ej.17.1.200 (version: 4.4)

Sequencing trimming using cutadapt

Custom Parameters
  • --report = minimal
  • -m = 70
  • --trim-n
  • --max-n = 1
  • -b = file:/mmfs1/projects/rrc_shared/common/references/adapters/illumina_adapters.fa
Method: Quality trimming

Quality trimming based on quality threshold and length parameters.

Custom Parameters
  • min length = 70
Method: Adapter trimming

Adapter/primer sequences were trimmed from the reads.

Custom Parameters
  • adapter = AGATCGGAAGAGCACACGTCTGAACTCCAGTCA
  • adapter = AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
  • adapter = TGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
  • adapter = ACACTCTTTCCCTACACGACGCTCTTCCGATCT
Method: Abiguous nucleotide trimming

Ambiguous nucleotides (N) were trimmed from the ends and reads with internal ambiguous nucleotides were discarded.

Custom Parameters
  • max-n = 1

Figure 1 . Trimming results

Table 1 . Trimming statistics

Table 1 . Trimming statistics
Sample Trim passed Ambig dropped Trim filter dropped Trim length dropped Percent passed Mean Length Mean Trimmed Length Quality trimmed bp
SRR22405441 998916 1084 0 0 99.89% 302.0 301.6 0
SRR22405442 999093 907 0 0 99.91% 302.0 301.6 0
SRR22405445 999098 902 0 0 99.91% 302.0 301.6 0
SRR22405453 999209 791 0 0 99.92% 302.0 301.6 0
SRR22405460 999065 935 0 0 99.91% 302.0 301.6 0
SRR22405461 999104 896 0 0 99.91% 302.0 301.6 0
SRR22405467 999137 863 0 0 99.91% 302.0 301.6 0
SRR22405532 999248 752 0 0 99.92% 302.0 301.6 0
SRR22405538 999247 753 0 0 99.92% 302.0 301.6 0
SRR22405544 999069 931 0 0 99.91% 302.0 301.6 0

  • Denovo genome assembly using short read sequence data

Method: Ultra-fast and memory-efficient (meta-)genome assemblerLi, D., Liu, C-M., Luo, R., Sadakane, K., and Lam, T-W., (2015) MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics, https://doi.org/10.1093/bioinformatics/btv033Li, D., Luo, R., Liu, C.M., Leung, C.M., Ting, H.F., Sadakane, K., Yamashita, H. and Lam, T.W., 2016. MEGAHIT v1.0: A Fast and Scalable Metagenome Assembler driven by Advanced Methodologies and Community Practices. Methods.

MEGAHIT is an ultra-fast and memory-efficient NGS assembler. It is optimized for metagenomes, but also works well on generic single genome assembly (small or mammalian size) and single-cell assembly.

Figure 1 . Coverage plot

Table 1 . Assembly summaries by stage

Table 1 . Assembly summaries by stage
Stage Count TotalLength Longest N50 N75 N90 L50 L75 L90
Raw assembly 98760 132988525 386035 4520 773 423 3293 25000 60951
Entropy filtered 17834 93478314 386035 17101 3588 1592 839 4193 10288
Alignment filtered 17834 93478314 386035 17101 3588 1592 839 4193 10288

Table 2 . Basic contig statistics, all contigs

Table 2 . Basic contig statistics, all contigs
#ID length GC coverage S_2mer S_3mer S_4mer
k141_76253 386035 51.891 25.04 0.995 0.992 0.988
k141_75754 349967 48.068 71.95 0.995 0.992 0.988
k141_57920 343211 43.778 14.91 0.991 0.987 0.984
k141_64647 342700 47.468 52.48 0.995 0.992 0.988
k141_41385 334399 46.144 42.69 0.995 0.992 0.988
k141_13355 299237 42.963 103.05 0.989 0.986 0.983
k141_72681 298100 46.238 47.34 0.994 0.991 0.987
k141_23679 297381 42.899 23.71 0.989 0.985 0.982
k141_57175 294754 53.046 25.54 0.995 0.991 0.988
k141_78529 290324 43.567 15.98 0.992 0.988 0.985
k141_67436 284177 59.188 15.93 0.980 0.976 0.971
k141_62849 260876 51.135 89.65 0.996 0.993 0.990
k141_74447 260643 47.583 52.42 0.996 0.992 0.989
k141_83857 254966 56.175 44.12 0.985 0.977 0.971
k141_68918 254313 51.474 85.44 0.996 0.993 0.989
k141_75851 250564 60.446 19.12 0.977 0.972 0.968
k141_96790 248942 44.240 18.86 0.991 0.986 0.982
k141_81690 247234 49.724 82.75 0.996 0.993 0.990
k141_42535 244785 45.550 45.19 0.993 0.990 0.986
k141_79983 237758 59.367 18.46 0.980 0.976 0.971
k141_5738 231031 43.386 20.93 0.990 0.986 0.983
k141_27065 221594 47.236 53.59 0.996 0.993 0.989
k141_2430 219676 44.398 17.06 0.991 0.986 0.982
k141_44990 216979 42.710 95.56 0.987 0.984 0.981
k141_84488 215400 45.105 48.75 0.991 0.988 0.984
k141_51746 212672 45.597 23.28 0.994 0.989 0.985
k141_66960 212292 42.070 14.21 0.988 0.985 0.982
k141_1896 206776 52.656 24.64 0.994 0.990 0.986
k141_48411 197638 42.358 15.04 0.988 0.985 0.982
k141_81702 187223 52.728 25.19 0.995 0.990 0.987
k141_38583 184883 47.834 44.12 0.996 0.993 0.989
k141_72242 183611 59.859 16.98 0.979 0.974 0.970
k141_24815 181053 42.652 23.43 0.988 0.984 0.981
k141_26408 181034 55.255 36.49 0.987 0.979 0.973
k141_15925 179431 59.056 18.45 0.980 0.975 0.970
k141_33573 176101 52.275 24.67 0.994 0.990 0.986
k141_44001 175853 45.180 13.75 0.995 0.991 0.987
k141_97315 174227 44.188 16.41 0.991 0.987 0.983
k141_62322 171919 52.663 64.31 0.996 0.993 0.989
k141_2659 171869 42.870 89.49 0.989 0.986 0.983
k141_82550 171455 44.813 107.52 0.992 0.989 0.986
k141_96587 170655 55.980 15.11 0.991 0.986 0.982
k141_35172 169718 56.888 15.50 0.989 0.985 0.980
k141_75756 166254 54.122 14.72 0.994 0.991 0.987
k141_41374 165793 43.850 24.56 0.990 0.985 0.982
k141_91043 164123 44.333 20.28 0.991 0.986 0.983
k141_77785 163397 43.819 15.28 0.991 0.987 0.984
k141_41990 159435 46.194 57.08 0.994 0.990 0.987
k141_11803 158681 42.615 17.22 0.988 0.984 0.981
k141_80897 158391 55.843 15.79 0.988 0.984 0.980
k141_21790 157936 51.005 20.85 0.994 0.991 0.987
k141_32728 156971 59.596 14.16 0.979 0.974 0.970
k141_95027 155923 43.156 23.90 0.989 0.985 0.981
k141_51324 154384 46.888 51.19 0.995 0.992 0.988
k141_3591 153662 43.432 90.19 0.990 0.987 0.984
k141_84380 153142 42.690 15.93 0.989 0.984 0.981
k141_59355 152608 42.628 116.29 0.989 0.986 0.983
k141_84999 151527 43.632 15.37 0.990 0.987 0.983
k141_45123 151251 45.147 15.59 0.993 0.989 0.985
k141_27114 150924 44.891 19.94 0.993 0.989 0.985
k141_96741 149946 59.731 15.46 0.979 0.974 0.969
k141_13370 149705 42.163 102.80 0.988 0.985 0.981
k141_42977 149580 47.624 11.11 0.996 0.993 0.990
k141_27525 147621 43.711 108.17 0.990 0.987 0.984
k141_71910 142594 51.029 82.99 0.995 0.992 0.988
k141_46683 142375 41.815 106.17 0.987 0.984 0.981
k141_99143 142287 43.939 98.40 0.991 0.988 0.984
k141_48485 141763 42.184 105.33 0.987 0.984 0.980
k141_54150 136465 56.687 39.04 0.985 0.976 0.970
k141_21559 135421 59.063 17.53 0.979 0.974 0.970
k141_95464 135091 56.597 17.36 0.987 0.982 0.979
k141_28940 133070 49.678 75.77 0.995 0.992 0.988
k141_88380 132617 45.896 22.78 0.993 0.990 0.987
k141_91187 132166 43.645 105.77 0.989 0.986 0.982
k141_13979 131559 55.999 42.28 0.985 0.977 0.971
k141_51155 130310 40.701 93.17 0.984 0.981 0.978
k141_59644 130099 43.392 22.81 0.990 0.986 0.982
k141_45536 128383 41.062 21.22 0.984 0.980 0.977
k141_82466 128221 50.585 18.59 0.993 0.989 0.985
k141_26706 123846 43.918 17.95 0.992 0.988 0.985
k141_90647 123773 51.598 20.19 0.992 0.988 0.984
k141_51365 122240 58.581 15.27 0.981 0.977 0.972
k141_55885 122187 49.666 17.58 0.992 0.988 0.984
k141_15292 122120 50.239 19.00 0.994 0.990 0.986
k141_53738 121746 50.261 28.09 0.996 0.992 0.989
k141_10239 121245 49.061 87.37 0.997 0.994 0.990
k141_42824 121088 47.904 50.61 0.996 0.993 0.989
k141_63114 119854 56.956 15.03 0.987 0.984 0.979
k141_60396 119376 41.513 21.65 0.985 0.982 0.978
k141_64994 119182 46.954 21.95 0.995 0.992 0.989
k141_50366 116680 49.949 17.93 0.995 0.992 0.989
k141_2361 115178 42.488 18.40 0.988 0.985 0.981
k141_23822 113096 52.944 86.43 0.995 0.992 0.987
k141_9556 112899 50.967 82.12 0.995 0.992 0.987
k141_27126 111928 42.784 19.77 0.988 0.983 0.980
k141_83769 111178 57.975 13.67 0.985 0.981 0.977
k141_53064 110986 58.216 13.63 0.983 0.978 0.974
k141_48989 110719 42.642 23.86 0.989 0.985 0.982
k141_57282 109773 47.991 22.59 0.995 0.991 0.988
k141_83539 108052 47.107 50.54 0.995 0.991 0.987
k141_70448 106598 42.593 93.82 0.988 0.985 0.982
k141_51033 106536 43.321 114.55 0.990 0.986 0.983
k141_5065 105585 53.258 26.02 0.996 0.992 0.988
k141_83497 105003 53.173 25.54 0.995 0.991 0.987
k141_24781 104179 48.006 49.05 0.996 0.992 0.987
k141_93750 104038 51.698 87.95 0.997 0.994 0.990
k141_92237 103058 42.712 21.29 0.988 0.983 0.980
k141_16289 102470 49.339 77.09 0.996 0.993 0.989
k141_95994 102259 44.355 12.85 0.993 0.989 0.985
k141_28171 102246 51.984 24.35 0.996 0.992 0.989
k141_69802 100834 46.324 47.84 0.993 0.989 0.985
k141_77299 100649 44.045 18.48 0.991 0.986 0.983
k141_41991 100016 42.449 109.82 0.987 0.984 0.981
k141_54260 98611 41.167 19.03 0.986 0.982 0.979
k141_37824 98169 49.635 22.71 0.996 0.993 0.990
k141_89631 96475 43.396 22.19 0.990 0.986 0.982
k141_84836 96469 51.144 19.29 0.993 0.990 0.986
k141_13312 95969 54.683 35.00 0.988 0.977 0.971
k141_9062 95611 42.234 20.42 0.986 0.982 0.979
k141_13881 93673 44.244 15.47 0.993 0.989 0.985
k141_36151 92350 54.744 15.01 0.992 0.987 0.983
k141_83423 92097 46.030 44.27 0.993 0.990 0.986
k141_69353 91819 51.926 25.08 0.994 0.990 0.986
k141_22905 91155 45.924 48.94 0.992 0.988 0.984
k141_13398 90938 44.397 17.11 0.990 0.985 0.981
k141_53223 90881 48.547 94.12 0.996 0.993 0.989
k141_88745 90510 56.724 41.20 0.983 0.974 0.968
k141_34670 90004 56.279 15.62 0.990 0.985 0.981
k141_36624 89692 53.772 12.18 0.994 0.991 0.988
k141_13718 89188 44.223 90.99 0.991 0.988 0.985
k141_27903 88763 57.652 15.71 0.988 0.984 0.979
k141_90466 87926 42.046 111.37 0.987 0.984 0.981
k141_6990 87173 44.237 14.92 0.992 0.988 0.984
k141_75562 86661 43.464 16.91 0.990 0.985 0.982
k141_94103 86515 53.057 23.06 0.993 0.989 0.985
k141_61400 86454 41.184 12.34 0.986 0.982 0.979
k141_2787 86200 51.450 75.86 0.995 0.992 0.987
k141_1475 85903 39.908 18.90 0.980 0.977 0.974
k141_92773 85546 54.970 15.49 0.988 0.983 0.979
k141_77771 85296 55.862 36.58 0.985 0.977 0.970
k141_21868 85138 45.083 110.87 0.992 0.988 0.985
k141_57883 84646 51.024 24.16 0.996 0.992 0.988
k141_39594 84614 35.912 49.60 0.963 0.958 0.954
k141_97888 83402 43.096 21.65 0.989 0.984 0.979
k141_4849 82819 55.090 34.75 0.991 0.987 0.984
k141_50406 81974 55.830 34.03 0.986 0.977 0.971
k141_54988 81821 42.415 22.68 0.987 0.983 0.979
k141_13491 81420 41.395 90.54 0.986 0.984 0.980
k141_52350 81384 64.865 13.65 0.961 0.957 0.951
k141_91525 81285 53.550 22.94 0.992 0.989 0.984
k141_78567 80719 42.082 14.26 0.988 0.984 0.981
k141_41578 80405 58.367 15.30 0.981 0.976 0.971
k141_61381 80387 56.054 14.66 0.986 0.982 0.978
k141_3562 80201 40.598 17.87 0.983 0.980 0.977
k141_45384 79781 41.907 96.00 0.986 0.983 0.980
k141_95479 78463 45.776 18.98 0.994 0.989 0.984
k141_88066 78312 43.320 12.13 0.990 0.987 0.983
k141_43196 78264 46.719 51.39 0.995 0.991 0.987
k141_89926 78120 41.852 22.71 0.986 0.981 0.978
k141_82546 77910 45.975 15.85 0.993 0.989 0.986
k141_55028 77458 44.700 18.73 0.993 0.989 0.985
k141_6004 77113 43.125 23.35 0.990 0.987 0.984
k141_51955 76803 49.157 44.86 0.996 0.993 0.988
k141_17618 76740 40.658 11.50 0.984 0.981 0.979
k141_52116 76728 57.822 42.33 0.983 0.974 0.968
k141_31256 76690 43.735 15.35 0.990 0.987 0.983
k141_93206 76151 55.318 43.19 0.987 0.978 0.972
k141_80039 76009 42.990 111.32 0.989 0.986 0.983
k141_51465 75523 56.493 37.17 0.986 0.976 0.970
k141_52342 75121 47.984 22.33 0.995 0.992 0.989
k141_66384 75099 53.561 13.97 0.993 0.988 0.984
k141_61447 74975 49.931 96.84 0.997 0.995 0.991
k141_26693 74893 56.276 42.14 0.983 0.974 0.968
k141_27630 74523 43.733 15.09 0.991 0.987 0.983
k141_81465 74238 41.145 24.36 0.984 0.979 0.976
k141_56436 74036 46.715 15.21 0.996 0.992 0.989
k141_27028 72914 54.493 48.25 0.990 0.981 0.975
k141_9059 71637 43.108 112.67 0.990 0.987 0.984
k141_48745 71566 59.732 17.27 0.979 0.974 0.968
k141_32013 69867 54.332 40.17 0.988 0.981 0.975
k141_66279 69627 55.629 36.75 0.988 0.982 0.977
k141_18164 69550 55.449 37.69 0.988 0.979 0.973
k141_12256 69444 43.459 14.16 0.991 0.988 0.984
k141_51108 69195 44.114 21.66 0.990 0.987 0.984
k141_63776 69127 42.147 110.80 0.987 0.983 0.980
k141_86889 68840 45.521 50.88 0.993 0.990 0.986
k141_11095 68757 41.164 21.55 0.983 0.978 0.975
k141_36739 68739 47.573 74.20 0.995 0.992 0.988
k141_83325 68686 59.672 13.52 0.979 0.975 0.970
k141_26953 68638 44.822 92.89 0.991 0.987 0.983
k141_52504 68586 43.108 11.02 0.990 0.986 0.983
k141_93931 68551 56.476 15.75 0.991 0.987 0.982
k141_80946 68049 52.543 22.39 0.993 0.990 0.986
k141_34272 68038 63.279 16.27 0.967 0.963 0.958
k141_5978 67970 55.463 14.29 0.991 0.988 0.983
k141_78523 67417 44.078 22.32 0.990 0.985 0.981
k141_22568 67042 52.695 24.61 0.996 0.992 0.988
k141_12268 66824 53.011 22.45 0.992 0.989 0.985
k141_11985 66786 46.450 12.08 0.993 0.991 0.988
k141_98546 66704 43.903 119.34 0.991 0.988 0.985
k141_86435 66463 51.358 13.53 0.996 0.991 0.987
k141_36442 65879 42.655 96.61 0.988 0.985 0.981
k141_44244 65839 40.614 15.61 0.982 0.978 0.976
k141_89671 65573 56.209 39.65 0.986 0.977 0.970
k141_46837 65540 44.173 13.94 0.991 0.988 0.985
k141_69521 65169 43.871 17.51 0.992 0.988 0.985
k141_32143 65103 44.376 99.09 0.992 0.988 0.985
k141_99779 64722 42.024 13.04 0.989 0.986 0.984
k141_79532 64415 52.750 11.47 0.992 0.988 0.984
k141_89562 64269 54.319 16.12 0.993 0.989 0.985
k141_89014 63846 47.496 53.99 0.995 0.992 0.988
k141_97942 63740 53.106 12.14 0.992 0.989 0.985
k141_59551 63526 50.530 25.35 0.995 0.992 0.988
k141_1921 63424 56.644 39.72 0.982 0.973 0.966
k141_44349 62988 44.342 15.47 0.991 0.987 0.984
k141_62130 62798 42.962 95.98 0.990 0.988 0.985
k141_25546 62664 43.993 42.53 0.990 0.987 0.984
k141_31934 62354 44.894 12.88 0.993 0.989 0.985
k141_48671 61911 43.231 110.38 0.988 0.985 0.982
k141_882 61831 46.643 102.30 0.994 0.991 0.987
k141_25274 61337 56.460 35.09 0.986 0.978 0.972
k141_88472 61296 55.284 33.94 0.987 0.978 0.972
k141_93630 61117 42.024 87.78 0.988 0.985 0.981
k141_33489 60876 52.181 24.32 0.994 0.989 0.985
k141_22066 60876 52.592 13.67 0.995 0.990 0.986
k141_39223 60443 56.582 34.53 0.986 0.978 0.972
k141_45916 60181 53.038 12.67 0.992 0.988 0.984
k141_16304 60064 58.724 14.14 0.983 0.978 0.973
k141_38700 59867 43.206 23.65 0.989 0.985 0.981
k141_1101 59800 45.682 14.45 0.995 0.990 0.986
k141_62948 59776 43.967 16.20 0.991 0.987 0.983
k141_88285 59458 46.885 30.01 0.995 0.993 0.989
k141_52756 59372 55.351 14.67 0.991 0.987 0.983
k141_13698 59208 42.369 21.43 0.987 0.984 0.980
k141_3163 59040 44.245 14.19 0.993 0.989 0.985
k141_64578 58995 39.717 12.92 0.981 0.977 0.974
k141_89647 58841 43.057 23.56 0.989 0.984 0.981
k141_63997 58656 49.441 75.63 0.995 0.992 0.988
k141_15714 58605 44.111 11.81 0.990 0.986 0.982
k141_94447 58591 46.157 18.04 0.992 0.987 0.983
k141_55609 58509 42.349 169.24 0.985 0.980 0.974
k141_90412 58491 47.691 46.68 0.995 0.992 0.987
k141_80891 58481 57.391 45.24 0.984 0.975 0.968
k141_26449 58239 47.501 16.59 0.993 0.990 0.986
k141_89856 58051 52.056 20.77 0.990 0.986 0.981
k141_47008 58003 53.904 14.40 0.990 0.985 0.981
k141_55895 57948 53.117 21.82 0.991 0.987 0.983
k141_83351 57717 59.624 14.83 0.978 0.973 0.968
k141_37860 57700 42.738 117.03 0.988 0.985 0.982
k141_8199 57599 64.234 12.71 0.963 0.958 0.953
k141_29216 56919 56.514 39.30 0.984 0.976 0.969
k141_67448 56866 60.894 16.97 0.976 0.972 0.968
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k141_67461 3055 49.034 8.69 0.994 0.989 0.980
k141_49454 3055 41.538 5.90 0.970 0.962 0.952
k141_64828 3054 50.524 6.24 0.993 0.982 0.970
k141_74324 3052 41.776 4.75 0.956 0.948 0.935
k141_43642 3052 54.161 16.14 0.989 0.981 0.971
k141_36049 3050 46.918 44.36 0.991 0.985 0.974
k141_35817 3049 32.765 6.22 0.953 0.950 0.944
k141_14711 3049 59.134 5.93 0.983 0.973 0.960
k141_40605 3048 47.605 5.25 0.991 0.986 0.976
k141_23317 3048 33.924 7.72 0.938 0.925 0.910
k141_97799 3047 29.865 5.83 0.928 0.919 0.908
k141_2387 3047 52.872 6.64 0.994 0.985 0.973
k141_64983 3046 47.045 5.77 0.988 0.977 0.966
k141_22942 3045 51.067 6.32 0.982 0.971 0.959
k141_13959 3045 45.025 7.58 0.990 0.984 0.975
k141_23975 3044 55.618 5.25 0.970 0.963 0.949
k141_63075 3043 52.613 200.44 0.989 0.982 0.972
k141_12483 3043 45.613 7.45 0.990 0.984 0.975
k141_17561 3042 53.123 175.84 0.994 0.989 0.980
k141_13920 3042 58.481 6.07 0.972 0.959 0.944
k141_1585 3041 48.767 6.26 0.983 0.971 0.960
k141_73223 3040 46.612 8.32 0.989 0.984 0.976
k141_66648 3039 54.623 6.84 0.989 0.980 0.969
k141_39826 3039 44.620 7.48 0.989 0.984 0.975
Displaying first 5000 of 98761 lines

Table 3 . Basic contig statistics, filtered contigs

Table 3 . Basic contig statistics, filtered contigs
#ID length GC coverage S_2mer S_3mer S_4mer
k141_76253 386035 51.891 25.07 0.995 0.992 0.988
k141_75754 349967 48.068 72.00 0.995 0.992 0.988
k141_57920 343211 43.778 14.94 0.991 0.987 0.984
k141_64647 342700 47.468 52.60 0.995 0.992 0.988
k141_41385 334399 46.144 42.71 0.995 0.992 0.988
k141_13355 299237 42.963 103.08 0.989 0.986 0.983
k141_72681 298100 46.238 47.37 0.994 0.991 0.987
k141_23679 297381 42.899 23.73 0.989 0.985 0.982
k141_57175 294754 53.046 25.60 0.995 0.991 0.988
k141_78529 290324 43.567 16.03 0.992 0.988 0.985
k141_67436 284177 59.188 15.97 0.980 0.976 0.971
k141_62849 260876 51.135 89.77 0.996 0.993 0.990
k141_74447 260643 47.583 52.43 0.996 0.992 0.989
k141_83857 254966 56.175 45.04 0.985 0.977 0.971
k141_68918 254313 51.474 85.52 0.996 0.993 0.989
k141_75851 250564 60.446 19.20 0.977 0.972 0.968
k141_96790 248942 44.240 18.89 0.991 0.986 0.982
k141_81690 247234 49.724 83.71 0.996 0.993 0.990
k141_42535 244785 45.550 45.20 0.993 0.990 0.986
k141_79983 237758 59.367 18.56 0.980 0.976 0.971
k141_5738 231031 43.386 20.94 0.990 0.986 0.983
k141_27065 221594 47.236 53.66 0.996 0.993 0.989
k141_2430 219676 44.398 17.16 0.991 0.986 0.982
k141_44990 216979 42.710 95.58 0.987 0.984 0.981
k141_84488 215400 45.105 48.78 0.991 0.988 0.984
k141_51746 212672 45.597 23.30 0.994 0.989 0.985
k141_66960 212292 42.070 14.27 0.988 0.985 0.982
k141_1896 206776 52.656 24.67 0.994 0.990 0.986
k141_48411 197638 42.358 15.11 0.988 0.985 0.982
k141_81702 187223 52.728 25.26 0.995 0.990 0.987
k141_38583 184883 47.834 44.19 0.996 0.993 0.989
k141_72242 183611 59.859 17.37 0.979 0.974 0.970
k141_24815 181053 42.652 23.44 0.988 0.984 0.981
k141_26408 181034 55.255 37.47 0.987 0.979 0.973
k141_15925 179431 59.056 18.56 0.980 0.975 0.970
k141_33573 176101 52.275 24.71 0.994 0.990 0.986
k141_44001 175853 45.180 13.81 0.995 0.991 0.987
k141_97315 174227 44.188 16.42 0.991 0.987 0.983
k141_62322 171919 52.663 64.42 0.996 0.993 0.989
k141_2659 171869 42.870 89.76 0.989 0.986 0.983
k141_82550 171455 44.813 107.52 0.992 0.989 0.986
k141_96587 170655 55.980 15.16 0.991 0.986 0.982
k141_35172 169718 56.888 15.60 0.989 0.985 0.980
k141_75756 166254 54.122 14.82 0.994 0.991 0.987
k141_41374 165793 43.850 24.57 0.990 0.985 0.982
k141_91043 164123 44.333 20.78 0.991 0.986 0.983
k141_77785 163397 43.819 15.31 0.991 0.987 0.984
k141_41990 159435 46.194 57.12 0.994 0.990 0.987
k141_11803 158681 42.615 17.25 0.988 0.984 0.981
k141_80897 158391 55.843 15.86 0.988 0.984 0.980
k141_21790 157936 51.005 20.90 0.994 0.991 0.987
k141_32728 156971 59.596 14.22 0.979 0.974 0.970
k141_95027 155923 43.156 23.92 0.989 0.985 0.981
k141_51324 154384 46.888 51.21 0.995 0.992 0.988
k141_3591 153662 43.432 90.19 0.990 0.987 0.984
k141_84380 153142 42.690 15.95 0.989 0.984 0.981
k141_59355 152608 42.628 116.35 0.989 0.986 0.983
k141_84999 151527 43.632 15.40 0.990 0.987 0.983
k141_45123 151251 45.147 15.63 0.993 0.989 0.985
k141_27114 150924 44.891 19.95 0.993 0.989 0.985
k141_96741 149946 59.731 15.54 0.979 0.974 0.969
k141_13370 149705 42.163 102.94 0.988 0.985 0.981
k141_42977 149580 47.624 11.12 0.996 0.993 0.990
k141_27525 147621 43.711 111.33 0.990 0.987 0.984
k141_71910 142594 51.029 83.04 0.995 0.992 0.988
k141_46683 142375 41.815 106.18 0.987 0.984 0.981
k141_99143 142287 43.939 98.42 0.991 0.988 0.984
k141_48485 141763 42.184 105.34 0.987 0.984 0.980
k141_54150 136465 56.687 40.01 0.985 0.976 0.970
k141_21559 135421 59.063 17.79 0.979 0.974 0.970
k141_95464 135091 56.597 17.39 0.987 0.982 0.979
k141_28940 133070 49.678 75.87 0.995 0.992 0.988
k141_88380 132617 45.896 22.89 0.993 0.990 0.987
k141_91187 132166 43.645 105.87 0.989 0.986 0.982
k141_13979 131559 55.999 43.27 0.985 0.977 0.971
k141_51155 130310 40.701 93.21 0.984 0.981 0.978
k141_59644 130099 43.392 22.82 0.990 0.986 0.982
k141_45536 128383 41.062 21.23 0.984 0.980 0.977
k141_82466 128221 50.585 18.63 0.993 0.989 0.985
k141_26706 123846 43.918 17.97 0.992 0.988 0.985
k141_90647 123773 51.598 20.24 0.992 0.988 0.984
k141_51365 122240 58.581 15.35 0.981 0.977 0.972
k141_55885 122187 49.666 17.62 0.992 0.988 0.984
k141_15292 122120 50.239 19.07 0.994 0.990 0.986
k141_53738 121746 50.261 28.21 0.996 0.992 0.989
k141_10239 121245 49.061 87.41 0.997 0.994 0.990
k141_42824 121088 47.904 50.64 0.996 0.993 0.989
k141_63114 119854 56.956 15.11 0.987 0.984 0.979
k141_60396 119376 41.513 21.66 0.985 0.982 0.978
k141_64994 119182 46.954 21.98 0.995 0.992 0.989
k141_50366 116680 49.949 18.01 0.995 0.992 0.989
k141_2361 115178 42.488 18.42 0.988 0.985 0.981
k141_23822 113096 52.944 86.49 0.995 0.992 0.987
k141_9556 112899 50.967 82.20 0.995 0.992 0.987
k141_27126 111928 42.784 21.72 0.988 0.983 0.980
k141_83769 111178 57.975 13.71 0.985 0.981 0.977
k141_53064 110986 58.216 13.72 0.983 0.978 0.974
k141_48989 110719 42.642 23.88 0.989 0.985 0.982
k141_57282 109773 47.991 22.64 0.995 0.991 0.988
k141_83539 108052 47.107 50.64 0.995 0.991 0.987
k141_70448 106598 42.593 93.87 0.988 0.985 0.982
k141_51033 106536 43.321 114.56 0.990 0.986 0.983
k141_5065 105585 53.258 26.79 0.996 0.992 0.988
k141_83497 105003 53.173 25.65 0.995 0.991 0.987
k141_24781 104179 48.006 49.06 0.996 0.992 0.987
k141_93750 104038 51.698 87.99 0.997 0.994 0.990
k141_92237 103058 42.712 21.32 0.988 0.983 0.980
k141_16289 102470 49.339 77.13 0.996 0.993 0.989
k141_95994 102259 44.355 12.87 0.993 0.989 0.985
k141_28171 102246 51.984 24.47 0.996 0.992 0.989
k141_69802 100834 46.324 47.85 0.993 0.989 0.985
k141_77299 100649 44.045 18.49 0.991 0.986 0.983
k141_41991 100016 42.449 110.34 0.987 0.984 0.981
k141_54260 98611 41.167 19.11 0.986 0.982 0.979
k141_37824 98169 49.635 22.81 0.996 0.993 0.990
k141_89631 96475 43.396 22.21 0.990 0.986 0.982
k141_84836 96469 51.144 19.35 0.993 0.990 0.986
k141_13312 95969 54.683 35.04 0.988 0.977 0.971
k141_9062 95611 42.234 20.44 0.986 0.982 0.979
k141_13881 93673 44.244 15.51 0.993 0.989 0.985
k141_36151 92350 54.744 15.83 0.992 0.987 0.983
k141_83423 92097 46.030 44.32 0.993 0.990 0.986
k141_69353 91819 51.926 25.14 0.994 0.990 0.986
k141_22905 91155 45.924 48.95 0.992 0.988 0.984
k141_13398 90938 44.397 17.12 0.990 0.985 0.981
k141_53223 90881 48.547 94.19 0.996 0.993 0.989
k141_88745 90510 56.724 41.98 0.983 0.974 0.968
k141_34670 90004 56.279 15.73 0.990 0.985 0.981
k141_36624 89692 53.772 12.20 0.994 0.991 0.988
k141_13718 89188 44.223 91.05 0.991 0.988 0.985
k141_27903 88763 57.652 15.81 0.988 0.984 0.979
k141_90466 87926 42.046 111.43 0.987 0.984 0.981
k141_6990 87173 44.237 14.96 0.992 0.988 0.984
k141_75562 86661 43.464 16.92 0.990 0.985 0.982
k141_94103 86515 53.057 23.07 0.993 0.989 0.985
k141_61400 86454 41.184 12.42 0.986 0.982 0.979
k141_2787 86200 51.450 75.95 0.995 0.992 0.987
k141_1475 85903 39.908 19.07 0.980 0.977 0.974
k141_92773 85546 54.970 15.51 0.988 0.983 0.979
k141_77771 85296 55.862 37.53 0.985 0.977 0.970
k141_21868 85138 45.083 111.39 0.992 0.988 0.985
k141_57883 84646 51.024 24.24 0.996 0.992 0.988
k141_39594 84614 35.912 49.67 0.963 0.958 0.954
k141_97888 83402 43.096 21.67 0.989 0.984 0.979
k141_4849 82819 55.090 34.98 0.991 0.987 0.984
k141_50406 81974 55.830 35.09 0.986 0.977 0.971
k141_54988 81821 42.415 22.69 0.987 0.983 0.979
k141_13491 81420 41.395 90.98 0.986 0.984 0.980
k141_52350 81384 64.865 13.76 0.961 0.957 0.951
k141_91525 81285 53.550 22.98 0.992 0.989 0.984
k141_78567 80719 42.082 14.32 0.988 0.984 0.981
k141_41578 80405 58.367 15.37 0.981 0.976 0.971
k141_61381 80387 56.054 14.72 0.986 0.982 0.978
k141_3562 80201 40.598 17.89 0.983 0.980 0.977
k141_45384 79781 41.907 96.06 0.986 0.983 0.980
k141_95479 78463 45.776 18.98 0.994 0.989 0.984
k141_88066 78312 43.320 12.16 0.990 0.987 0.983
k141_43196 78264 46.719 51.41 0.995 0.991 0.987
k141_89926 78120 41.852 22.72 0.986 0.981 0.978
k141_82546 77910 45.975 15.88 0.993 0.989 0.986
k141_55028 77458 44.700 18.74 0.993 0.989 0.985
k141_6004 77113 43.125 23.36 0.990 0.987 0.984
k141_51955 76803 49.157 44.88 0.996 0.993 0.988
k141_17618 76740 40.658 11.53 0.984 0.981 0.979
k141_52116 76728 57.822 43.00 0.983 0.974 0.968
k141_31256 76690 43.735 15.42 0.990 0.987 0.983
k141_93206 76151 55.318 44.24 0.987 0.978 0.972
k141_80039 76009 42.990 111.36 0.989 0.986 0.983
k141_51465 75523 56.493 37.27 0.986 0.976 0.970
k141_52342 75121 47.984 22.56 0.995 0.992 0.989
k141_66384 75099 53.561 14.01 0.993 0.988 0.984
k141_61447 74975 49.931 96.87 0.997 0.995 0.991
k141_26693 74893 56.276 43.54 0.983 0.974 0.968
k141_27630 74523 43.733 15.15 0.991 0.987 0.983
k141_81465 74238 41.145 24.37 0.984 0.979 0.976
k141_56436 74036 46.715 15.86 0.996 0.992 0.989
k141_27028 72914 54.493 49.21 0.990 0.981 0.975
k141_9059 71637 43.108 112.72 0.990 0.987 0.984
k141_48745 71566 59.732 17.39 0.979 0.974 0.968
k141_32013 69867 54.332 41.53 0.988 0.981 0.975
k141_66279 69627 55.629 37.32 0.988 0.982 0.977
k141_18164 69550 55.449 37.75 0.988 0.979 0.973
k141_12256 69444 43.459 14.18 0.991 0.988 0.984
k141_51108 69195 44.114 29.23 0.990 0.987 0.984
k141_63776 69127 42.147 110.83 0.987 0.983 0.980
k141_86889 68840 45.521 50.89 0.993 0.990 0.986
k141_11095 68757 41.164 21.59 0.983 0.978 0.975
k141_36739 68739 47.573 74.24 0.995 0.992 0.988
k141_83325 68686 59.672 14.28 0.979 0.975 0.970
k141_26953 68638 44.822 93.35 0.991 0.987 0.983
k141_52504 68586 43.108 11.03 0.990 0.986 0.983
k141_93931 68551 56.476 15.78 0.991 0.987 0.982
k141_80946 68049 52.543 22.51 0.993 0.990 0.986
k141_34272 68038 63.279 16.31 0.967 0.963 0.958
k141_5978 67970 55.463 14.33 0.991 0.988 0.983
k141_78523 67417 44.078 22.37 0.990 0.985 0.981
k141_22568 67042 52.695 24.78 0.996 0.992 0.988
k141_12268 66824 53.011 22.48 0.992 0.989 0.985
k141_11985 66786 46.450 12.15 0.993 0.991 0.988
k141_98546 66704 43.903 119.35 0.991 0.988 0.985
k141_86435 66463 51.358 13.57 0.996 0.991 0.987
k141_36442 65879 42.655 96.66 0.988 0.985 0.981
k141_44244 65839 40.614 15.64 0.982 0.978 0.976
k141_89671 65573 56.209 39.70 0.986 0.977 0.970
k141_46837 65540 44.173 13.96 0.991 0.988 0.985
k141_69521 65169 43.871 20.76 0.992 0.988 0.985
k141_32143 65103 44.376 99.12 0.992 0.988 0.985
k141_99779 64722 42.024 13.06 0.989 0.986 0.984
k141_79532 64415 52.750 11.88 0.992 0.988 0.984
k141_89562 64269 54.319 16.19 0.993 0.989 0.985
k141_89014 63846 47.496 53.99 0.995 0.992 0.988
k141_97942 63740 53.106 12.17 0.992 0.989 0.985
k141_59551 63526 50.530 25.39 0.995 0.992 0.988
k141_1921 63424 56.644 41.35 0.982 0.973 0.966
k141_44349 62988 44.342 15.48 0.991 0.987 0.984
k141_62130 62798 42.962 97.58 0.990 0.988 0.985
k141_25546 62664 43.993 42.57 0.990 0.987 0.984
k141_31934 62354 44.894 12.92 0.993 0.989 0.985
k141_48671 61911 43.231 110.52 0.988 0.985 0.982
k141_882 61831 46.643 102.32 0.994 0.991 0.987
k141_25274 61337 56.460 35.86 0.986 0.978 0.972
k141_88472 61296 55.284 34.64 0.987 0.978 0.972
k141_93630 61117 42.024 90.21 0.988 0.985 0.981
k141_33489 60876 52.181 24.37 0.994 0.989 0.985
k141_22066 60876 52.592 13.70 0.995 0.990 0.986
k141_39223 60443 56.582 35.34 0.986 0.978 0.972
k141_45916 60181 53.038 12.69 0.992 0.988 0.984
k141_16304 60064 58.724 14.25 0.983 0.978 0.973
k141_38700 59867 43.206 23.73 0.989 0.985 0.981
k141_1101 59800 45.682 14.46 0.995 0.990 0.986
k141_62948 59776 43.967 16.21 0.991 0.987 0.983
k141_88285 59458 46.885 30.34 0.995 0.993 0.989
k141_52756 59372 55.351 14.76 0.991 0.987 0.983
k141_13698 59208 42.369 21.45 0.987 0.984 0.980
k141_3163 59040 44.245 14.22 0.993 0.989 0.985
k141_64578 58995 39.717 12.96 0.981 0.977 0.974
k141_89647 58841 43.057 26.68 0.989 0.984 0.981
k141_63997 58656 49.441 76.48 0.995 0.992 0.988
k141_15714 58605 44.111 11.83 0.990 0.986 0.982
k141_94447 58591 46.157 18.08 0.992 0.987 0.983
k141_55609 58509 42.349 169.28 0.985 0.980 0.974
k141_90412 58491 47.691 46.70 0.995 0.992 0.987
k141_80891 58481 57.391 46.54 0.984 0.975 0.968
k141_26449 58239 47.501 16.60 0.993 0.990 0.986
k141_89856 58051 52.056 20.81 0.990 0.986 0.981
k141_47008 58003 53.904 14.43 0.990 0.985 0.981
k141_55895 57948 53.117 21.86 0.991 0.987 0.983
k141_83351 57717 59.624 14.89 0.978 0.973 0.968
k141_37860 57700 42.738 118.52 0.988 0.985 0.982
k141_8199 57599 64.234 12.76 0.963 0.958 0.953
k141_29216 56919 56.514 40.07 0.984 0.976 0.969
k141_67448 56866 60.894 17.48 0.976 0.972 0.968
k141_36401 56716 41.692 15.54 0.985 0.981 0.978
k141_29378 56616 56.044 36.65 0.986 0.977 0.971
k141_84974 56557 51.842 22.66 0.996 0.992 0.988
k141_56426 56533 60.324 14.89 0.976 0.970 0.964
k141_87892 55816 44.729 17.51 0.992 0.988 0.984
k141_85804 55733 49.606 36.99 0.995 0.989 0.985
k141_69360 55635 59.155 16.11 0.980 0.975 0.970
k141_74224 55279 55.234 37.41 0.985 0.974 0.966
k141_37789 55114 55.944 14.07 0.990 0.985 0.981
k141_48353 55094 47.276 20.65 0.995 0.990 0.986
k141_91412 54927 48.625 54.71 0.996 0.991 0.987
k141_6585 54718 44.742 22.20 0.991 0.987 0.983
k141_14621 54374 41.494 113.14 0.986 0.983 0.980
k141_18939 54341 49.515 32.72 0.986 0.980 0.973
k141_53418 53846 46.167 49.38 0.994 0.990 0.986
k141_48275 53749 44.717 51.68 0.992 0.988 0.984
k141_40053 53036 62.220 16.69 0.971 0.967 0.962
k141_47152 52537 58.277 15.50 0.986 0.982 0.977
k141_40539 52015 47.075 17.66 0.995 0.990 0.986
k141_20278 51872 58.457 14.43 0.982 0.978 0.973
k141_90877 51794 55.829 13.35 0.990 0.986 0.982
k141_88437 51625 44.922 11.53 0.993 0.989 0.985
k141_67899 51602 43.386 13.93 0.989 0.985 0.982
k141_81733 51555 45.988 47.03 0.994 0.990 0.986
k141_51267 51329 43.506 117.80 0.990 0.986 0.983
k141_65199 50981 52.280 22.70 0.991 0.988 0.984
k141_6369 50824 48.105 12.25 0.990 0.985 0.981
k141_49284 50459 53.087 23.89 0.995 0.990 0.986
k141_78216 49972 44.275 10.87 0.992 0.988 0.984
k141_77281 49828 56.819 38.75 0.983 0.976 0.971
k141_90790 49751 43.601 15.35 0.990 0.985 0.981
k141_61635 49587 52.869 12.72 0.995 0.991 0.987
k141_43375 49503 42.559 11.45 0.989 0.986 0.983
k141_6150 49297 40.516 20.67 0.984 0.980 0.976
k141_51311 49212 45.798 45.49 0.993 0.989 0.985
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k141_37752 3219 53.464 6.46 0.991 0.982 0.970
k141_99787 3218 51.181 6.58 0.993 0.986 0.977
k141_7352 3218 53.045 13.05 0.986 0.979 0.968
k141_63084 3218 38.844 8.77 0.966 0.961 0.952
k141_60123 3218 42.573 6.09 0.989 0.985 0.977
k141_33169 3218 57.924 5.81 0.982 0.973 0.962
k141_37810 3217 52.782 7.28 0.991 0.984 0.974
k141_34236 3217 49.549 23.67 0.995 0.988 0.980
k141_2762 3217 37.644 7.75 0.972 0.968 0.958
k141_91208 3216 51.897 7.71 0.984 0.975 0.961
k141_24097 3216 54.664 5.60 0.990 0.980 0.967
k141_37526 3215 58.414 4.88 0.976 0.965 0.951
k141_27129 3215 44.572 20.16 0.986 0.979 0.969
k141_70326 3214 44.493 7.30 0.988 0.983 0.975
k141_18878 3213 39.527 7.21 0.979 0.974 0.965
k141_71866 3212 57.472 6.81 0.986 0.976 0.964
k141_5141 3212 43.555 6.43 0.985 0.975 0.966
k141_51516 3211 56.400 7.09 0.988 0.978 0.966
k141_25617 3210 54.237 10.77 0.990 0.982 0.972
k141_72228 3209 36.740 6.76 0.968 0.964 0.957
k141_23720 3209 48.800 7.23 0.978 0.968 0.957
k141_93366 3207 56.657 8.04 0.983 0.972 0.960
k141_83604 3207 41.815 71.21 0.986 0.980 0.972
k141_7340 3207 55.722 28.13 0.989 0.977 0.963
k141_89504 3206 52.308 5.16 0.988 0.977 0.966
k141_87641 3206 44.635 7.34 0.992 0.987 0.979
k141_30080 3206 45.945 7.75 0.973 0.962 0.950
k141_60050 3205 57.660 6.29 0.987 0.979 0.969
k141_16039 3204 47.222 6.75 0.994 0.991 0.983
k141_23012 3203 54.667 6.99 0.985 0.975 0.963
k141_41596 3202 47.626 11.98 0.993 0.988 0.978
k141_35689 3202 51.187 5.35 0.993 0.984 0.975
k141_84250 3201 51.515 30.40 0.993 0.981 0.970
k141_12803 3201 54.608 6.31 0.991 0.984 0.974
k141_74880 3200 43.344 5.56 0.986 0.979 0.969
k141_73198 3199 32.698 5.15 0.937 0.931 0.923
k141_60246 3199 43.170 8.27 0.988 0.985 0.977
k141_26685 3199 43.889 15.22 0.985 0.975 0.962
k141_22866 3199 42.451 8.55 0.985 0.981 0.975
k141_99134 3198 44.028 7.05 0.987 0.982 0.973
k141_91850 3198 45.841 6.65 0.982 0.977 0.968
k141_66441 3198 48.624 7.78 0.993 0.989 0.981
k141_30652 3198 49.625 54.39 0.989 0.983 0.971
k141_62145 3197 53.550 6.42 0.991 0.985 0.975
k141_41084 3197 52.831 6.64 0.994 0.987 0.978
k141_66639 3196 54.850 5.42 0.979 0.969 0.956
k141_5294 3196 41.771 12.46 0.985 0.980 0.971
k141_98427 3195 53.521 9.36 0.978 0.965 0.954
k141_92681 3195 46.792 10.35 0.986 0.977 0.967
k141_30634 3195 49.077 12.77 0.990 0.984 0.972
k141_15086 3195 52.582 5.34 0.992 0.983 0.973
k141_91198 3192 54.292 10.33 0.991 0.981 0.965
k141_68176 3192 47.243 8.93 0.987 0.978 0.968
k141_1324 3191 48.543 9.00 0.994 0.988 0.979
k141_87281 3190 36.583 7.89 0.969 0.966 0.960
k141_87133 3190 48.025 36.96 0.991 0.986 0.977
k141_44707 3190 33.824 37.78 0.950 0.945 0.932
k141_48524 3189 53.998 8.08 0.993 0.985 0.975
k141_38203 3189 53.402 6.83 0.992 0.984 0.974
k141_80417 3188 50.376 6.23 0.991 0.982 0.971
k141_60586 3188 50.659 9.10 0.993 0.989 0.980
k141_70141 3187 52.024 8.13 0.992 0.988 0.979
k141_91287 3186 55.869 217.90 0.986 0.977 0.966
k141_75616 3186 55.681 11.05 0.989 0.983 0.975
k141_18633 3185 53.375 5.90 0.989 0.982 0.971
k141_91620 3184 48.964 10.54 0.986 0.974 0.962
k141_77335 3184 56.407 5.38 0.975 0.963 0.950
k141_35271 3184 29.083 6.93 0.928 0.921 0.911
k141_7055 3183 50.927 8.78 0.996 0.988 0.976
k141_83973 3181 44.231 6.64 0.991 0.988 0.980
k141_72794 3181 53.568 7.42 0.979 0.967 0.955
k141_41129 3181 50.047 7.21 0.992 0.981 0.971
k141_30616 3181 46.558 15.06 0.981 0.971 0.959
k141_77100 3180 59.403 7.65 0.974 0.961 0.947
k141_98449 3179 42.812 7.28 0.975 0.965 0.953
k141_65521 3179 33.501 4.97 0.946 0.938 0.927
k141_67917 3174 51.103 6.03 0.987 0.976 0.963
k141_45192 3174 38.217 6.71 0.969 0.963 0.955
k141_73399 3173 57.390 7.02 0.980 0.965 0.948
k141_98335 3172 28.846 6.50 0.917 0.905 0.893
k141_28278 3170 46.246 8.39 0.994 0.990 0.983
k141_98494 3169 39.066 7.26 0.970 0.965 0.958
k141_26027 3169 48.028 78.72 0.990 0.987 0.981
k141_95978 3168 40.278 6.63 0.980 0.975 0.967
k141_70288 3168 62.973 8.98 0.962 0.955 0.944
k141_57531 3167 34.828 16.78 0.959 0.951 0.939
k141_47339 3167 64.888 6.41 0.960 0.953 0.943
k141_87709 3166 40.082 8.96 0.973 0.967 0.960
k141_82422 3166 53.348 7.53 0.986 0.978 0.966
k141_62906 3166 57.517 7.19 0.983 0.974 0.961
k141_59712 3166 37.018 8.02 0.966 0.962 0.955
k141_51941 3166 50.063 273.86 0.993 0.987 0.976
k141_49194 3166 49.842 6.76 0.994 0.988 0.979
k141_47268 3166 36.260 8.17 0.966 0.959 0.950
k141_88093 3165 54.439 6.22 0.990 0.982 0.971
k141_40935 3165 41.264 8.42 0.982 0.978 0.969
k141_53017 3164 37.579 12.28 0.972 0.968 0.958
k141_452 3164 33.502 5.71 0.941 0.928 0.912
k141_39947 3164 59.893 5.95 0.979 0.969 0.956
k141_94034 3161 41.885 454.68 0.972 0.966 0.956
k141_6606 3161 49.668 8.89 0.986 0.975 0.963
k141_60726 3160 41.646 5.92 0.968 0.959 0.947
k141_30805 3160 52.184 5.78 0.994 0.987 0.978
k141_89557 3157 53.595 10.71 0.993 0.988 0.978
k141_88140 3157 56.351 7.62 0.988 0.978 0.965
k141_86556 3156 48.606 14.45 0.991 0.985 0.976
k141_44241 3156 41.381 8.02 0.982 0.977 0.968
k141_87452 3153 50.682 9.49 0.991 0.977 0.966
k141_54949 3152 58.312 7.88 0.952 0.940 0.926
k141_32889 3152 39.150 7.04 0.974 0.970 0.963
k141_80036 3150 45.175 7.52 0.985 0.978 0.968
k141_4720 3150 48.667 10.93 0.986 0.976 0.962
k141_94507 3149 53.700 10.52 0.992 0.985 0.973
k141_92935 3149 57.637 6.78 0.983 0.973 0.962
k141_551 3148 50.032 7.25 0.985 0.975 0.966
k141_25144 3148 48.666 5.34 0.992 0.986 0.977
k141_42975 3146 48.093 6.91 0.993 0.988 0.978
k141_69076 3144 49.809 6.14 0.992 0.984 0.975
k141_66847 3144 46.501 8.46 0.990 0.982 0.971
k141_77272 3143 40.757 5.99 0.969 0.963 0.955
k141_39365 3143 50.970 8.19 0.970 0.959 0.946
k141_93955 3142 34.087 5.20 0.958 0.954 0.945
k141_19631 3142 38.701 6.25 0.978 0.974 0.968
k141_40452 3140 48.280 6.01 0.993 0.981 0.969
k141_97227 3138 52.199 21.67 0.986 0.980 0.970
k141_75765 3138 50.319 11.32 0.995 0.991 0.983
k141_23068 3138 37.412 7.13 0.971 0.968 0.962
k141_98585 3137 33.918 6.51 0.946 0.935 0.921
k141_59772 3136 41.295 8.32 0.979 0.974 0.965
k141_41489 3136 38.616 9.71 0.973 0.969 0.960
k141_99851 3135 41.978 17.08 0.980 0.973 0.966
k141_87667 3135 37.448 6.62 0.968 0.963 0.956
k141_35281 3135 46.061 10.58 0.990 0.981 0.972
k141_32300 3135 48.644 5.09 0.993 0.985 0.975
k141_14542 3135 46.507 18.76 0.993 0.987 0.975
k141_61313 3134 42.597 6.81 0.988 0.983 0.974
k141_97848 3133 50.686 12.69 0.996 0.991 0.981
k141_2323 3133 55.027 9.33 0.972 0.958 0.945
k141_62451 3130 51.693 7.95 0.987 0.979 0.970
k141_91489 3129 47.299 6.84 0.992 0.982 0.971
k141_53730 3129 50.815 13.64 0.990 0.979 0.967
k141_65505 3127 52.638 5.15 0.995 0.987 0.976
k141_57454 3125 51.584 5.73 0.994 0.985 0.976
k141_94237 3123 43.612 7.92 0.985 0.977 0.968
k141_86373 3123 48.415 6.04 0.982 0.972 0.956
k141_16868 3123 47.711 90.04 0.995 0.992 0.985
k141_82751 3122 53.812 27.34 0.985 0.975 0.962
k141_22351 3122 52.819 8.25 0.981 0.969 0.958
k141_74391 3121 32.426 5.79 0.940 0.931 0.919
k141_8835 3119 41.969 6.25 0.987 0.982 0.974
k141_35188 3119 43.988 15.83 0.989 0.984 0.977
k141_12272 3118 33.900 7.77 0.940 0.936 0.928
k141_96160 3117 55.951 7.45 0.984 0.974 0.963
k141_92557 3116 53.049 10.38 0.991 0.985 0.976
k141_15319 3116 50.513 10.64 0.994 0.987 0.976
k141_64613 3115 50.883 7.71 0.996 0.989 0.979
k141_674 3114 51.541 7.08 0.993 0.983 0.971
k141_32157 3114 30.379 5.64 0.928 0.915 0.901
k141_61735 3113 52.168 9.96 0.991 0.986 0.976
k141_7665 3112 43.766 21.05 0.986 0.976 0.965
k141_90735 3111 42.269 8.87 0.986 0.979 0.970
k141_40067 3111 40.791 8.82 0.983 0.979 0.971
k141_29047 3111 37.287 6.02 0.961 0.954 0.944
k141_76260 3110 54.952 6.37 0.985 0.975 0.964
k141_32949 3110 40.611 8.05 0.981 0.977 0.969
k141_22753 3110 63.215 11.73 0.962 0.952 0.939
k141_47262 3109 46.156 9.71 0.990 0.981 0.971
k141_63860 3108 53.057 8.14 0.995 0.990 0.982
k141_41940 3108 47.426 6.33 0.988 0.983 0.974
k141_97812 3107 47.473 8.00 0.978 0.968 0.957
k141_50805 3107 55.391 5.08 0.984 0.976 0.965
k141_68517 3106 52.737 5.45 0.990 0.980 0.970
k141_34546 3105 40.676 12.06 0.974 0.969 0.960
k141_86894 3104 35.599 6.39 0.957 0.948 0.938
k141_24715 3104 55.219 25.19 0.990 0.982 0.969
k141_70579 3103 53.432 16.55 0.985 0.977 0.968
k141_18989 3103 25.814 16.30 0.904 0.901 0.894
k141_59364 3102 49.001 14.03 0.991 0.986 0.976
k141_20406 3102 52.257 6.36 0.990 0.980 0.968
k141_78957 3101 37.955 7.91 0.958 0.953 0.945
k141_58911 3100 44.065 28.98 0.989 0.983 0.973
k141_16548 3100 46.613 6.80 0.985 0.977 0.965
k141_74610 3097 46.529 9.92 0.984 0.973 0.961
k141_42997 3097 52.050 11.17 0.988 0.977 0.964
k141_26589 3097 54.246 8.99 0.976 0.964 0.952
k141_2370 3097 58.541 12.72 0.979 0.969 0.958
k141_183 3097 46.787 12.57 0.992 0.982 0.971
k141_68108 3096 41.505 8.73 0.972 0.966 0.958
k141_5459 3096 50.904 8.23 0.993 0.987 0.979
k141_30629 3095 49.273 6.61 0.986 0.977 0.967
k141_22946 3095 53.376 7.70 0.992 0.986 0.976
k141_5965 3094 32.321 5.01 0.943 0.934 0.921
k141_1928 3094 48.578 8.18 0.994 0.990 0.982
k141_93923 3093 46.848 18.82 0.995 0.986 0.977
k141_43606 3093 48.400 6.82 0.980 0.973 0.963
k141_54402 3092 50.291 5.70 0.991 0.984 0.974
k141_62120 3091 51.019 7.93 0.980 0.965 0.951
k141_4897 3091 49.790 8.79 0.979 0.969 0.958
k141_40600 3091 31.834 6.55 0.934 0.922 0.910
k141_26815 3091 56.519 6.26 0.977 0.960 0.944
k141_51812 3090 48.803 7.47 0.993 0.987 0.976
k141_51052 3090 60.032 7.03 0.974 0.963 0.951
k141_50086 3090 46.278 11.08 0.991 0.981 0.969
k141_3108 3090 59.482 5.31 0.977 0.967 0.953
k141_76488 3089 51.182 10.15 0.985 0.974 0.961
k141_41086 3089 46.811 7.64 0.989 0.979 0.969
k141_29219 3089 50.049 7.04 0.993 0.985 0.975
k141_90605 3088 46.567 22.26 0.992 0.989 0.982
k141_91798 3087 35.536 7.01 0.964 0.961 0.955
k141_78409 3087 53.320 6.70 0.989 0.981 0.969
k141_29720 3087 53.191 6.96 0.993 0.984 0.973
k141_20545 3087 47.133 7.83 0.984 0.979 0.970
k141_5854 3086 52.333 6.48 0.989 0.980 0.970
k141_5781 3086 37.492 7.46 0.966 0.958 0.949
k141_44632 3086 51.167 7.92 0.989 0.984 0.975
k141_15093 3086 46.111 7.80 0.991 0.987 0.980
k141_95383 3085 46.872 10.31 0.993 0.988 0.979
k141_22928 3085 51.183 4.02 0.995 0.987 0.975
k141_85444 3084 41.991 15.01 0.986 0.982 0.974
k141_13555 3084 47.795 6.41 0.982 0.975 0.965
k141_28641 3082 36.729 8.73 0.966 0.963 0.956
k141_1331 3082 52.920 5.74 0.992 0.985 0.976
k141_40353 3081 56.378 6.21 0.983 0.975 0.964
k141_96322 3080 54.610 6.42 0.991 0.984 0.974
k141_98834 3079 51.120 7.66 0.987 0.980 0.971
k141_15229 3079 44.755 9.40 0.990 0.985 0.976
k141_23384 3078 49.838 7.01 0.995 0.983 0.972
k141_84638 3076 51.463 11.84 0.993 0.984 0.973
k141_84566 3076 53.316 6.23 0.991 0.984 0.971
k141_16827 3076 48.147 7.35 0.993 0.989 0.981
k141_66053 3075 49.431 31.32 0.989 0.983 0.974
k141_52651 3075 40.943 23.89 0.971 0.966 0.957
k141_18269 3075 56.780 7.09 0.974 0.967 0.957
k141_11876 3075 38.276 5.41 0.975 0.973 0.966
k141_69734 3074 29.506 8.11 0.933 0.930 0.922
k141_1355 3074 59.889 8.61 0.976 0.966 0.952
k141_7235 3073 38.432 10.20 0.971 0.966 0.957
k141_31776 3073 51.025 4.62 0.993 0.986 0.976
k141_9458 3072 43.294 7.50 0.986 0.981 0.973
k141_73752 3071 31.879 6.65 0.942 0.935 0.924
k141_57524 3071 58.808 10.40 0.982 0.975 0.965
k141_48676 3071 38.294 156.76 0.971 0.963 0.950
k141_70754 3070 38.143 7.05 0.973 0.970 0.963
k141_3124 3070 53.941 6.39 0.983 0.972 0.958
k141_26046 3070 56.352 7.93 0.986 0.981 0.971
k141_11727 3070 42.508 6.32 0.985 0.979 0.972
k141_76177 3069 53.926 10.27 0.992 0.988 0.980
k141_46177 3069 51.450 7.17 0.994 0.987 0.976
k141_32173 3069 55.002 8.07 0.981 0.966 0.953
k141_75492 3068 37.647 6.97 0.974 0.971 0.963
k141_73429 3068 48.142 8.86 0.989 0.979 0.969
k141_56306 3068 43.351 37.96 0.985 0.977 0.965
k141_37713 3068 60.267 5.69 0.968 0.958 0.946
k141_14097 3067 40.985 6.48 0.978 0.972 0.963
k141_91708 3066 52.511 7.45 0.983 0.977 0.968
k141_57630 3066 49.772 8.85 0.993 0.983 0.974
k141_92298 3064 47.552 14.13 0.993 0.986 0.977
k141_87715 3064 43.342 9.48 0.970 0.964 0.956
k141_86547 3064 50.033 7.31 0.987 0.974 0.961
k141_4600 3064 46.736 37.76 0.986 0.982 0.974
k141_17970 3064 40.960 9.92 0.983 0.978 0.970
k141_40431 3063 48.482 8.34 0.991 0.987 0.980
k141_22713 3063 52.628 6.51 0.991 0.981 0.970
k141_87145 3062 28.805 9.87 0.930 0.927 0.921
k141_17015 3062 50.425 15.24 0.986 0.980 0.973
k141_92792 3061 35.707 4.59 0.962 0.958 0.950
k141_18911 3061 34.237 6.75 0.945 0.934 0.921
k141_87185 3060 50.196 19.56 0.982 0.973 0.959
k141_69596 3060 60.033 6.15 0.971 0.958 0.941
k141_84707 3059 58.320 7.43 0.980 0.969 0.953
k141_41595 3059 44.426 17.80 0.988 0.980 0.969
k141_27807 3058 38.522 7.53 0.975 0.971 0.962
k141_46420 3057 63.363 15.76 0.964 0.954 0.940
k141_79454 3056 45.746 5.07 0.987 0.975 0.964
k141_71791 3056 38.973 7.76 0.968 0.964 0.957
k141_67156 3056 54.777 11.82 0.986 0.978 0.968
k141_96026 3055 46.972 5.62 0.993 0.984 0.974
k141_67461 3055 49.034 15.95 0.994 0.989 0.980
k141_49454 3055 41.538 6.86 0.970 0.962 0.952
k141_64828 3054 50.524 6.25 0.993 0.982 0.970
k141_74324 3052 41.776 4.77 0.956 0.948 0.935
k141_43642 3052 54.161 28.83 0.989 0.981 0.971
k141_36049 3050 46.918 371.05 0.991 0.985 0.974
k141_35817 3049 32.765 6.23 0.953 0.950 0.944
k141_14711 3049 59.134 8.33 0.983 0.973 0.960
k141_40605 3048 47.605 5.26 0.991 0.986 0.976
k141_23317 3048 33.924 13.15 0.938 0.925 0.910
k141_97799 3047 29.865 5.85 0.928 0.919 0.908
k141_2387 3047 52.872 6.64 0.994 0.985 0.973
k141_64983 3046 47.045 5.78 0.988 0.977 0.966
k141_22942 3045 51.067 7.18 0.982 0.971 0.959
k141_13959 3045 45.025 7.58 0.990 0.984 0.975
k141_23975 3044 55.618 5.28 0.970 0.963 0.949
k141_63075 3043 52.613 226.20 0.989 0.982 0.972
k141_12483 3043 45.613 7.45 0.990 0.984 0.975
k141_17561 3042 53.123 184.32 0.994 0.989 0.980
k141_13920 3042 58.481 6.26 0.972 0.959 0.944
k141_1585 3041 48.767 6.31 0.983 0.971 0.960
k141_73223 3040 46.612 9.58 0.989 0.984 0.976
k141_66648 3039 54.623 6.85 0.989 0.980 0.969
k141_39826 3039 44.620 7.50 0.989 0.984 0.975
Displaying first 5000 of 17835 lines

Method: eggNOG MapperCarlos P. Cantalapiedra, Ana Hernandez-Plaza, Ivica Letunic, Peer Bork, Jaime Huerta-Cepas. (2021). eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Molecular Biology and Evolution, msab293, https://doi.org/10.1093/molbev/msab293

Fast genome-wide functional annotation through orthology assignment.

Custom Parameters
  • --itype = metagenome
  • --genepred = prodigal
  • --decorate_gff = yes
Method: Bacterial ribosomal RNA predictor Seemann T. barrnap 0.9 : rapid ribosomal RNA prediction https://github.com/tseemann/barrnap

Barrnap predicts the location of ribosomal RNA genes in genomes. It supports bacteria (5S,23S,16S), archaea (5S,5.8S,23S,16S), metazoan mitochondria (12S,16S) and eukaryotes (5S,5.8S,28S,18S).

Custom Parameters
  • --outseq = alldata_annotation_rrna.fa
Method: ARAGORNLaslett D, Canback B. (2004) ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 32(1):11-16. https://doi.org/10.1093/nar/gkh152

a program to detect tRNA genes and tmRNA genes in nucleotide sequences

Custom Parameters
  • -m
  • -t
  • -l
  • -w
  • -seq
Method: CAT/BAT: tool for taxonomic classification of contigs and metagenome-assembled genomes (MAGs) von Meijenfeldt FAB, Arkhipova K, Cambuy DD, Coutinho FH, Dutilh BE. (2019) Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biology. 20:217. https://doi.org/10.1186/s13059-019-1817-x

Contig Annotation Tool (CAT) and Bin Annotation Tool (BAT) are pipelines for the taxonomic classification of long DNA sequences and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies.

Custom Parameters
  • database = /mmfs1/projects/rrc_shared/common/references/CAT/20240422_CAT_nr/db
  • taxonomy = /mmfs1/projects/rrc_shared/common/references/CAT/20240422_CAT_nr/tax

Table 1 . Annotation details

Table 1 . Annotation details
Feature Counts
CDS 93526
rRNA 112
tmRNA 17
tmRNA* 4
tRNA 1184

  • Binning assembled contigs

Method: MetaBAT2Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z. (2019) MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 7:e7359. doi: 10.7717/peerj.7359. PMID: 31388474; PMCID: PMC6662567.

A robust statistical framework for reconstructing genomes from metagenomic data

Custom Parameters
  • --saveCls
  • -l
Method: CAT/BAT: tool for taxonomic classification of contigs and metagenome-assembled genomes (MAGs) von Meijenfeldt FAB, Arkhipova K, Cambuy DD, Coutinho FH, Dutilh BE. (2019) Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biology. 20:217. https://doi.org/10.1186/s13059-019-1817-x

Contig Annotation Tool (CAT) and Bin Annotation Tool (BAT) are pipelines for the taxonomic classification of long DNA sequences and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies.

Custom Parameters
  • database = /mmfs1/projects/rrc_shared/common/references/CAT/20240422_CAT_nr/db
  • taxonomy = /mmfs1/projects/rrc_shared/common/references/CAT/20240422_CAT_nr/tax

Citations