step3_VI-subfam_in_azolla.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# outline\n",
    "\n",
    "goal: In this notebook I will try to find R2R3 MYBs from the VI subfamily as described in R&J in the Azolla annotation and genome (if the former is unsuccessfull).\n",
    "\n",
    "I have two resources to search with, the Salivina sequences termed VI by R&J, and the whole VI subfamily I extracted earlier via the R&J paper.\n",
    "\n",
    "Alternativelly, I may use the pfam or superfamily hmm models to find these genes, but that won't be as specific as I would like it to be.\n",
    "\n",
    "My resource to search in, is the Azolla filiculoides genome 1.2 and annotation v1.1 from fernbase.org"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plan 1\n",
    "First, I'll make an HMM of all VI sequences I have, and hmmsearch the azolla annotation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "mkdir data/genome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-07-31 14:21:41--  ftp://ftp.fernbase.org/Azolla_filiculoides/Azolla_asm_v1.1/Azolla_filiculoides.protein.highconfidence_v1.1.fasta\n",
      "           => ‘./data/genome/Azolla_filiculoides.protein.highconfidence_v1.1.fasta’\n",
      "Resolving ftp.fernbase.org (ftp.fernbase.org)... 132.236.81.244\n",
      "Connecting to ftp.fernbase.org (ftp.fernbase.org)|132.236.81.244|:21... connected.\n",
      "Logging in as anonymous ... Logged in!\n",
      "==> SYST ... done.    ==> PWD ... done.\n",
      "==> TYPE I ... done.  ==> CWD (1) /Azolla_filiculoides/Azolla_asm_v1.1 ... done.\n",
      "==> SIZE Azolla_filiculoides.protein.highconfidence_v1.1.fasta ... 9519909\n",
      "==> PASV ... done.    ==> RETR Azolla_filiculoides.protein.highconfidence_v1.1.fasta ... done.\n",
      "Length: 9519909 (9,1M) (unauthoritative)\n",
      "\n",
      "Azolla_filiculoides 100%[===================>]   9,08M  9,42MB/s    in 1,0s    \n",
      "\n",
      "2020-07-31 14:21:44 (9,42 MB/s) - ‘./data/genome/Azolla_filiculoides.protein.highconfidence_v1.1.fasta’ saved [9519909]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "wget -O ./data/genome/Azolla_filiculoides.protein.highconfidence_v1.1.fasta \\\n",
    "         ftp://ftp.fernbase.org/Azolla_filiculoides/Azolla_asm_v1.1/Azolla_filiculoides.protein.highconfidence_v1.1.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;34mdata/genome\u001b[00m\n",
      "└── Azolla_filiculoides.protein.highconfidence_v1.1.fasta\n",
      "\n",
      "0 directories, 1 file\n"
     ]
    }
   ],
   "source": [
    "tree data/genome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">g8575.t1 length:1504 (mRNA) (CHBRA15g00250)  (myb-related protein 305-like)\n",
      "MDSGGGAVVDADDSALPAQHQGQSHPQAHGQKVAAQQQSVGGGKGGGAGPAVCKSESDAAAAVGGAHGSSPLSLAGDEASAAAAGGKGVSGGAPAVGPKRPLKRGVWLPEEDEILKGYVANQGPKNWSSIETMGLLARSGKSCRLRWMNHLRPDLNRRSTKFTPEEEAIVVTKQKLHGNKWAQIAKSLSGRTDNDVKNFWNMRMKKLAKLARLEKQRQRQLLISQGSPIAMAAAAMGLPPGARMLASPGDVVVSFNPRTIAEAAAQQRLMVSSSAVPAHHHRLQNFCNASAGLAGNGGLTGMGTAGASIHALACGNGGGGAGSGSLGDIFASPSPKGYVHGSAMGDLLHVDPDCIMSTSSILNHHHHHPHHLSPHHHHIHHSLHGLSPTNGTCGTNGGTAGVSPTSAAAAAAAAAAAAALMSGCGDGRSGALSAALQASGKAIGGSGGVGDCSIANHHDPCGRLMTVGGALEHHHQHHHHHHQNQLLMQARGCGLLLHSGGGLHAVLNRWGDLKGQSLGGVGGRGSSDGDSVVPISAGGSPSFMQACGLATTPSAQCLMTPCTRPGSVLTGVGRTSPTCMPVHLSEMLKVHNSPVSLVCDMTTGKPTMDGLTKALGFAEPQSADVGDSGIGSGGVCLDGGVTTDCSTDPNMSSSCNSTESSGTHQPQYMMTGGSTGMAASGVPSCAAPLNGQGAMAPSVARAAAVTCLDGDVESPTVFRNNNMPRSTSAGTATVPAGAINLDVERNLVSNSNAFSTADRALEAVTNGKAAMSGGGNGSGASSSSPSHRHQQQQSVGCTASSKDLLDNLGIGVEGAVRSAVNGANIAAAIGQSLMPLDVCFGRAGVDVDAGLATMSESQATTMSAPPRMSPRMSSSNVTLRGQTTPVLNRSPSFTFSADMAATTPRSSSIAAGGTGTTLMVGGEKRQRAPGTVSDRVPTSRQQRSQADDNAFSSVAEHGNTGPSVVAMEATAAIDNLSPRERRVPLLSVAAQSRQKQEQLEQQQRQQQQQQEQQQQQQQEQQLQRGEFDRVPRCLPASPDFGTTTMRQFGGLASSGMGCAAATMAIMMAGRPDQALLMMAGGPTTGPELASLNAVVPSRQDFQAAPQEIGENRTDVQVDDEEEEEEEAEEEDDEEETAEVIDSKAIMDWDFGGIGVVPSGTRAWPGSTGAGMSGFLSGPQPVTLPSHDPRQQQQQHQHQHQQQQQQQQQQHHEQVRGLIARDARHVLLGQLEGDRDDEEMVSRVTCADLGCQPQSRSGHRRSQPQVRISCPNSDPDQDTAASMGSDSDPGFSDGMQVEPSKAMISGLRVGGVGSRYMDAAYGSTHPCASSAAVETSSPLTQTSRLQMRMPTTVGIHTQPTNGRRLAVGFSGHAHPQQEQQQHHYRFCSSSLLQTNGMRDATPPLGEAAVPPLRKRVETGDVCGVAGASALRYGGPPSAMTSVSHADTFRKGNDCLESNRLASVPVSAVLSGGDGSLSWAQQYNDLDTVFLDSEADSIERMLDSC*\n",
      ">BBG43567 LC221833 MYB transcription factor, partial [Chara australis]\n",
      "pkrplkrgvwlpeedeilkgyvanqgpknwssietmgllarsgkscrlrwmnhlrpdlnrrstkftpeeeaivvskqkdcgnkwaqiakflsgrtdndvknfwnmrmkklaklarlekqrqrqllisqgspiamaaaamglppgtrmlaspgdv\n",
      ">BBG43566 LC221832 Chara leptospora mRNA for MYB transcription factor, partial cds,strain: TAN136.\n",
      "PKRPLKRGVWLPEEDEILKGYVANQGPKNWSSIETMGLLARSGKSCRLRWMNHLRPDLNRRSTKFTPEEEAIVVAKQTEHGNRWAQIAKCLAGRTDNDVKNFWNMRMKKLAKLARLEKQRQRQL\n",
      ">Mapoly0019s0071.1\n",
      "MKTIQNGDEVLRKGPWMPEEDEILVEYVRQFGARDWSSIRSKGLLPRTGKSCRLRWVNKLKPDLKRSGCKFSPEEEKLVVEMQAKLGNKWAKIASCLPGRTDNDVKNFWSTRQKRILRALQRPKMPSGSTDAAADTSDLRKDPSITDYGAFQVHSPSGTGLVPSGFVTPFMFEDDNAFREPPGHLRLRHSVQPFNSLEVDDVLSLKLQDMVAGDSHFTDSDFSQCGRVLDDPRHGSVMYQHSPRMMITPRNARSVKRRPRSRRDARSCFEHHSGTDGGQDSVQMLPEIGLPLIQGNSRDFGVPLSTEQQNNNYLDFLLQTEASSMHSSQMFSPELPSFLTNSQQWEKLPMLYSNDPSIQEPDVKDSDSCSPDSVISGFAADVFDSLEPLCPAAPEWWVARLS\n",
      ">XM_024688730.1 >XP_024544498.1\n",
      "MSIKTVQVGDEVLRKGPWMPEEDEILVEYVRQYGPRDWSSIRSKGLLPRTGKSCRLRWVNKLKPDLKRSGCKFSPEEEKLVVEMQSKLGNKWAKIASCLPGRTDNDVKNFWSTRQKRLLRALQRPKTPSGKGDCSSLDLHDMNKEPLNEFSAFQSFGPPLFFGNEEEDNTFKGQSDEWKPSRQSLQAFGDLEDMEDVLTAKLPDIGPPDYPFPDTDFSNSNAAAVAAAAAAAATTGTPPAQHQHQQDQQQQQQGGATFCCSDLSGFLAGSPPQWEKLPLLCLDPDACIKEEGDSISPDSVLSGFPADVFDSLAPRFGPAKFTTRAARTHKAATKFFLLAMLQHGFGIWIYAARLDVPLVPFSRRDAHLLVVSGYAIFMKARFFSRR\n"
     ]
    }
   ],
   "source": [
    "head data/VI_sequences_linear.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# hmmbuild :: profile HMM construction from multiple sequence alignments\n",
      "# HMMER 3.3 (Nov 2019); http://hmmer.org/\n",
      "# Copyright (C) 2019 Howard Hughes Medical Institute.\n",
      "# Freely distributed under the BSD open source license.\n",
      "# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
      "Usage: hmmbuild [-options] <hmmfile_out> <msafile>\n",
      "\n",
      "Basic options:\n",
      "  -h     : show brief help on version and usage\n",
      "  -n <s> : name the HMM <s>\n",
      "  -o <f> : direct summary output to file <f>, not stdout\n",
      "  -O <f> : resave annotated, possibly modified MSA to file <f>\n",
      "\n",
      "Options for selecting alphabet rather than guessing it:\n",
      "  --amino : input alignment is protein sequence data\n",
      "  --dna   : input alignment is DNA sequence data\n",
      "  --rna   : input alignment is RNA sequence data\n",
      "\n",
      "Alternative model construction strategies:\n",
      "  --fast           : assign cols w/ >= symfrac residues as consensus  [default]\n",
      "  --hand           : manual construction (requires reference annotation)\n",
      "  --symfrac <x>    : sets sym fraction controlling --fast construction  [0.5]\n",
      "  --fragthresh <x> : if L <= x*alen, tag sequence as a fragment  [0.5]\n",
      "\n",
      "Alternative relative sequence weighting strategies:\n",
      "  --wpb     : Henikoff position-based weights  [default]\n",
      "  --wgsc    : Gerstein/Sonnhammer/Chothia tree weights\n",
      "  --wblosum : Henikoff simple filter weights\n",
      "  --wnone   : don't do any relative weighting; set all to 1\n",
      "  --wgiven  : use weights as given in MSA file\n",
      "  --wid <x> : for --wblosum: set identity cutoff  [0.62]  (0<=x<=1)\n",
      "\n",
      "Alternative effective sequence weighting strategies:\n",
      "  --eent       : adjust eff seq # to achieve relative entropy target  [default]\n",
      "  --eclust     : eff seq # is # of single linkage clusters\n",
      "  --enone      : no effective seq # weighting: just use nseq\n",
      "  --eset <x>   : set eff seq # for all models to <x>\n",
      "  --ere <x>    : for --eent: set minimum rel entropy/position to <x>\n",
      "  --esigma <x> : for --eent: set sigma param to <x>  [45.0]\n",
      "  --eid <x>    : for --eclust: set fractional identity cutoff to <x>  [0.62]\n",
      "\n",
      "Alternative prior strategies:\n",
      "  --pnone    : don't use any prior; parameters are frequencies\n",
      "  --plaplace : use a Laplace +1 prior\n",
      "\n",
      "Handling single sequence inputs:\n",
      "  --singlemx    : use substitution score matrix for single-sequence inputs\n",
      "  --mx <s>      : substitution score matrix (built-in matrices, with --singlemx)\n",
      "  --mxfile <f>  : read substitution score matrix from file <f> (with --singlemx)\n",
      "  --popen <x>   : force gap open prob. (w/ --singlemx, aa default 0.02, nt 0.031)\n",
      "  --pextend <x> : force gap extend prob. (w/ --singlemx, aa default 0.4, nt 0.75)\n",
      "\n",
      "Control of E-value calibration:\n",
      "  --EmL <n> : length of sequences for MSV Gumbel mu fit  [200]  (n>0)\n",
      "  --EmN <n> : number of sequences for MSV Gumbel mu fit  [200]  (n>0)\n",
      "  --EvL <n> : length of sequences for Viterbi Gumbel mu fit  [200]  (n>0)\n",
      "  --EvN <n> : number of sequences for Viterbi Gumbel mu fit  [200]  (n>0)\n",
      "  --EfL <n> : length of sequences for Forward exp tail tau fit  [100]  (n>0)\n",
      "  --EfN <n> : number of sequences for Forward exp tail tau fit  [200]  (n>0)\n",
      "  --Eft <x> : tail mass for Forward exponential tail tau fit  [0.04]  (0<x<1)\n",
      "\n",
      "Other options:\n",
      "  --cpu <n>          : number of parallel CPU workers for multithreads  [2]\n",
      "  --stall            : arrest after start: for attaching debugger to process\n",
      "  --informat <s>     : assert input alifile is in format <s> (no autodetect)\n",
      "  --seed <n>         : set RNG seed to <n> (if 0: one-time arbitrary seed)  [42]\n",
      "  --w_beta <x>       : tail mass at which window length is determined\n",
      "  --w_length <n>     : window length \n",
      "  --maxinsertlen <n> : pretend all inserts are length <= <n>\n"
     ]
    }
   ],
   "source": [
    "hmmbuild -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# hmmbuild :: profile HMM construction from multiple sequence alignments\n",
      "# HMMER 3.3 (Nov 2019); http://hmmer.org/\n",
      "# Copyright (C) 2019 Howard Hughes Medical Institute.\n",
      "# Freely distributed under the BSD open source license.\n",
      "# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
      "# input alignment file:             ./data/alignments_raw/VI_sequences_linear_aligned-mafft-linsi.fasta\n",
      "# output HMM file:                  ./data/VI-sequences_linear_hmm-v1.hmm\n",
      "# number of worker threads:         4\n",
      "# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
      "\n",
      "# idx name                  nseq  alen  mlen eff_nseq re/pos description\n",
      "#---- -------------------- ----- ----- ----- -------- ------ -----------\n",
      "1     VI_sequences_linear_aligned-mafft-linsi    11  1791   414     1.55  0.590 \n",
      "\n",
      "# CPU time: 0.26u 0.00s 00:00:00.26 Elapsed: 00:00:00.26\n"
     ]
    }
   ],
   "source": [
    "hmmbuild --cpu $(nproc) ./data/VI-sequences_linear_hmm-v1.hmm ./data/alignments_raw/VI_sequences_linear_aligned-mafft-linsi.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working...    done.\n",
      "Pressed and indexed 1 HMMs (1 names).\n",
      "Models pressed into binary file:   data/VI-sequences_linear_hmm-v1.hmm.h3m\n",
      "SSI index for binary model file:   data/VI-sequences_linear_hmm-v1.hmm.h3i\n",
      "Profiles (MSV part) pressed into:  data/VI-sequences_linear_hmm-v1.hmm.h3f\n",
      "Profiles (remainder) pressed into: data/VI-sequences_linear_hmm-v1.hmm.h3p\n"
     ]
    }
   ],
   "source": [
    "hmmpress data/VI-sequences_linear_hmm-v1.hmm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "hmmscan --cpu $(nproc) \\\n",
    "        --tblout ./data/VI-sequences_linear_hmm-v1_Azfihits.tab \\\n",
    "        -o ./data/VI-sequences_linear_hmm-v1_Azfihits.log \\\n",
    "        ./data/VI-sequences_linear_hmm-v1.hmm \\\n",
    "        ./data/genome/Azolla_filiculoides.protein.highconfidence_v1.1.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azfi_s0076.g037908         9e-95  304.9 \n",
      "Azfi_s0021.g015882       4.5e-51  161.0 \n",
      "Azfi_s0041.g026722       4.3e-50  157.8 \n",
      "Azfi_s0004.g008455       1.9e-49  155.6 \n",
      "Azfi_s0035.g025508       4.2e-48  151.2 \n",
      "Azfi_s0010.g012145       1.7e-47  149.2 \n",
      "Azfi_s0008.g011612       6.6e-47  147.3 \n",
      "Azfi_s0129.g048859       8.1e-47  147.0 \n",
      "Azfi_s0005.g009119         5e-46  144.4 \n",
      "Azfi_s0005.g009154       5.5e-46  144.2 \n",
      "Azfi_s0812.g087843       9.4e-46  143.5 \n",
      "Azfi_s0061.g034930       3.6e-45  141.6 \n",
      "Azfi_s0445.g070637       5.6e-45  140.9 \n",
      "Azfi_s0001.g000839       1.9e-44  139.2 \n",
      "Azfi_s0006.g009830       5.4e-44  137.7 \n",
      "Azfi_s0014.g013584         1e-42  133.5 \n",
      "Azfi_s0003.g007581       4.1e-42  131.5 \n",
      "Azfi_s0010.g012085       4.9e-42  131.2 \n",
      "Azfi_s0003.g007699       6.2e-42  130.9 \n",
      "Azfi_s0390.g067689       9.9e-42  130.2 \n",
      "Azfi_s0016.g014344       1.8e-41  129.4 \n",
      "Azfi_s0113.g045874       3.4e-41  128.5 \n",
      "Azfi_s0007.g010866       4.8e-41  128.0 \n",
      "Azfi_s0217.g058506       4.8e-41  128.0 \n",
      "Azfi_s0938.g093183       4.7e-41  128.0 \n",
      "Azfi_s0041.g026717       1.5e-40  126.3 \n",
      "Azfi_s0243.g059882       2.8e-40  125.4 \n",
      "Azfi_s1083.g097159       2.9e-40  125.4 \n",
      "Azfi_s0018.g014830       3.8e-40  125.0 \n",
      "Azfi_s0629.g080127       8.5e-40  123.8 \n",
      "Azfi_s0173.g055749       1.1e-39  123.5 \n",
      "Azfi_s0163.g054240       2.2e-39  122.5 \n",
      "Azfi_s0125.g048539       2.6e-38  118.9 \n",
      "Azfi_s0130.g049059       3.2e-38  118.7 \n",
      "Azfi_s0103.g044681       4.1e-38  118.3 \n",
      "Azfi_s0001.g000218         3e-37  115.5 \n",
      "Azfi_s0014.g013539         4e-37  115.0 \n",
      "Azfi_s0105.g044929       1.4e-36  113.2 \n",
      "Azfi_s0179.g056264       3.5e-36  111.9 \n",
      "Azfi_s0193.g057153       4.4e-35  108.3 \n",
      "Azfi_s0460.g072042       4.4e-34  105.0 \n",
      "Azfi_s0179.g056258       1.9e-33  102.9 \n",
      "Azfi_s0138.g051134         2e-30   93.0 \n",
      "Azfi_s0008.g011623       7.8e-30   91.0 \n",
      "Azfi_s0001.g000538         1e-29   90.6 \n",
      "Azfi_s3508.g115947       8.1e-29   87.7 \n",
      "Azfi_s0130.g049054       7.6e-28   84.5 \n",
      "Azfi_s0145.g051658       8.8e-27   81.0 \n",
      "Azfi_s1025.g095454       2.3e-25   76.3 \n",
      "Azfi_s0164.g054291       6.7e-23   68.2 \n",
      "Azfi_s0009.g011773       2.9e-21   62.8 \n",
      "Azfi_s0007.g010874       4.5e-20   58.9 \n",
      "Azfi_s0009.g011981       3.7e-19   55.9 \n",
      "Azfi_s0254.g060469         2e-18   53.4 \n",
      "Azfi_s2035.g108729       1.4e-17   50.6 \n",
      "Azfi_s0003.g008076       1.7e-16   47.1 \n",
      "Azfi_s0100.g044336         9e-15   41.4 \n",
      "Azfi_s0002.g001484       6.9e-13   35.2 \n",
      "Azfi_s0022.g016182       3.4e-12   33.0 \n",
      "Azfi_s0059.g034687       5.3e-12   32.3 \n",
      "Azfi_s0059.g034689       4.3e-11   29.3 \n",
      "Azfi_s3609.g116385         4e-10   26.1 \n",
      "Azfi_s0078.g038139         1e-07   18.2 \n",
      "Azfi_s0113.g045914       9.9e-08   18.2 \n",
      "Azfi_s0380.g067373       2.1e-07   17.1 \n",
      "Azfi_s0185.g056637       7.9e-07   15.3 \n",
      "Azfi_s0088.g042510       9.4e-07   15.0 \n",
      "Azfi_s0343.g065739       2.6e-06   13.5 \n",
      "Azfi_s0008.g011626       3.7e-06   13.1 \n",
      "Azfi_s0345.g065826       4.6e-06   12.7 \n",
      "Azfi_s0292.g063413         5e-06   12.6 \n",
      "Azfi_s0003.g008081       6.3e-06   12.3 \n",
      "Azfi_s0028.g023921       1.1e-05   11.5 \n",
      "Azfi_s0063.g035389       3.1e-05   10.0 \n",
      "Azfi_s0007.g011036        0.0018    4.2 \n",
      "Azfi_s0076.g037915        0.0061    2.5 \n",
      "Azfi_s0010.g012066         0.058   -0.8 \n",
      "Azfi_s0730.g084085          0.13   -1.9 \n",
      "Azfi_s0158.g053847          0.25   -2.9 \n",
      "Azfi_s0354.g066687          0.99   -4.8 \n",
      "Azfi_s0092.g043090             1   -6.6 \n",
      "Azfi_s0064.g035559             1  -14.6 \n",
      "Azfi_s0032.g024768             1  -19.8 \n",
      "Azfi_s0230.g059134             1  -27.1 \n",
      "Azfi_s0019.g015298             1  -44.7 \n"
     ]
    }
   ],
   "source": [
    "#excuse this ugly visualisation, I don't have the patience\n",
    "grep -v '#'  data/VI-sequences_linear_hmm-v1_Azfihits.tab | cut -c 52-70,80-100 | sort -k3nr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### intermediate conclusion\n",
    "That's quite some results and even some quite good hits if you ask me.\n",
    "Before I proceed to extract these sequences, inspect the alignment with VI and VII subfamilies and make a phylogeny,\n",
    "I first want to do a quick blast with the salvinia sequences (nucleotide) against the Azolla filiculoides genome.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plan 2\n",
    "blast salvinia type VI sequences against Azolla filiculoides CDSs/genome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(blast) USAGE\n",
      "  makeblastdb [-h] [-help] [-in input_file] [-input_type type]\n",
      "    -dbtype molecule_type [-title database_title] [-parse_seqids]\n",
      "    [-hash_index] [-mask_data mask_data_files] [-mask_id mask_algo_ids]\n",
      "    [-mask_desc mask_algo_descriptions] [-gi_mask]\n",
      "    [-gi_mask_name gi_based_mask_names] [-out database_name]\n",
      "    [-max_file_sz number_of_bytes] [-logfile File_Name] [-taxid TaxID]\n",
      "    [-taxid_map TaxIDMapFile] [-version]\n",
      "\n",
      "DESCRIPTION\n",
      "   Application to create BLAST databases, version 2.5.0+\n",
      "\n",
      "Use '-help' to print detailed descriptions of command line arguments\n",
      "(blast) (base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "conda activate blast\n",
    "makeblastdb -h\n",
    "conda deactivate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(blast) (blast) (base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "conda activate blast\n",
    "makeblastdb -in ./data/genome/Azolla_filiculoides.protein.highconfidence_v1.1.fasta \\\n",
    "            -out data/genome/Azolla_filiculoides.protein.highconfidence_v1.1_blastdb \\\n",
    "            -title Azolla_filiculoides.protein.highconfidence_v1.1 \\\n",
    "            -logfile data/genome/Azolla_filiculoides.protein.highconfidence_v1.1_blastdb.log \\\n",
    "            -dbtype prot\n",
    "conda deactivate\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;34mdata/genome\u001b[00m\n",
      "├── Azolla_filiculoides.protein.highconfidence_v1.1_blastdb.log\n",
      "├── Azolla_filiculoides.protein.highconfidence_v1.1_blastdb.perf\n",
      "├── Azolla_filiculoides.protein.highconfidence_v1.1_blastdb.phr\n",
      "├── Azolla_filiculoides.protein.highconfidence_v1.1_blastdb.pin\n",
      "├── Azolla_filiculoides.protein.highconfidence_v1.1_blastdb.psq\n",
      "└── Azolla_filiculoides.protein.highconfidence_v1.1.fasta\n",
      "\n",
      "0 directories, 6 files\n",
      "(base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "tree data/genome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(blast) USAGE\n",
      "  blastp [-h] [-help] [-import_search_strategy filename]\n",
      "    [-export_search_strategy filename] [-task task_name] [-db database_name]\n",
      "    [-dbsize num_letters] [-gilist filename] [-seqidlist filename]\n",
      "    [-negative_gilist filename] [-entrez_query entrez_query]\n",
      "    [-db_soft_mask filtering_algorithm] [-db_hard_mask filtering_algorithm]\n",
      "    [-subject subject_input_file] [-subject_loc range] [-query input_file]\n",
      "    [-out output_file] [-evalue evalue] [-word_size int_value]\n",
      "    [-gapopen open_penalty] [-gapextend extend_penalty]\n",
      "    [-qcov_hsp_perc float_value] [-max_hsps int_value]\n",
      "    [-xdrop_ungap float_value] [-xdrop_gap float_value]\n",
      "    [-xdrop_gap_final float_value] [-searchsp int_value]\n",
      "    [-sum_stats bool_value] [-seg SEG_options] [-soft_masking soft_masking]\n",
      "    [-matrix matrix_name] [-threshold float_value] [-culling_limit int_value]\n",
      "    [-best_hit_overhang float_value] [-best_hit_score_edge float_value]\n",
      "    [-window_size int_value] [-lcase_masking] [-query_loc range]\n",
      "    [-parse_deflines] [-outfmt format] [-show_gis]\n",
      "    [-num_descriptions int_value] [-num_alignments int_value]\n",
      "    [-line_length line_length] [-html] [-max_target_seqs num_sequences]\n",
      "    [-num_threads int_value] [-ungapped] [-remote] [-comp_based_stats compo]\n",
      "    [-use_sw_tback] [-version]\n",
      "\n",
      "DESCRIPTION\n",
      "   Protein-Protein BLAST 2.5.0+\n",
      "\n",
      "Use '-help' to print detailed descriptions of command line arguments\n",
      "(blast) (base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "conda activate blast\n",
    "blastp -h\n",
    "conda deactivate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(blast) (blast) (base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "conda activate blast\n",
    "blastp -db ./data/genome/Azolla_filiculoides.protein.highconfidence_v1.1_blastdb \\\n",
    "       -query <(grep Sacu -A1 ./data/VI_sequences_linear.fasta) \\\n",
    "       -num_threads $(nproc) \\\n",
    "       -outfmt 6 \\\n",
    "       -out ./data/VI-sequences_linear_blasthits-v1.tab\n",
    "conda deactivate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azfi_s0076.g037908\t78.378\t185\t1.54e-94\t271\n",
      "Azfi_s0004.g008455\t42.958\t142\t3.03e-35\t129\n",
      "Azfi_s0021.g015882\t43.704\t135\t3.26e-34\t126\n",
      "Azfi_s0014.g013584\t47.619\t126\t2.20e-32\t120\n",
      "Azfi_s0129.g048859\t46.774\t124\t1.50e-31\t118\n",
      "Azfi_s0035.g025508\t41.216\t148\t1.78e-31\t114\n",
      "Azfi_s0005.g009154\t42.568\t148\t4.68e-31\t117\n",
      "Azfi_s0041.g026722\t45.902\t122\t7.06e-31\t115\n",
      "Azfi_s0008.g011612\t43.548\t124\t1.36e-30\t114\n",
      "Azfi_s0010.g012145\t44.628\t121\t3.50e-30\t114\n",
      "Azfi_s0005.g009119\t46.721\t122\t1.09e-29\t113\n",
      "Azfi_s0001.g000839\t40.157\t127\t1.58e-29\t108\n",
      "Azfi_s0016.g014344\t38.776\t147\t9.18e-29\t110\n",
      "Azfi_s0006.g009830\t42.149\t121\t1.24e-28\t107\n",
      "Azfi_s0003.g007581\t47.500\t120\t3.25e-28\t105\n",
      "Azfi_s0445.g070637\t44.715\t123\t4.48e-28\t107\n",
      "Azfi_s0938.g093183\t42.308\t130\t6.23e-28\t108\n",
      "Azfi_s0010.g012085\t39.259\t135\t9.76e-28\t107\n",
      "Azfi_s0163.g054240\t38.849\t139\t3.03e-27\t105\n",
      "Azfi_s0113.g045874\t38.519\t135\t4.67e-27\t105\n",
      "(base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "cut -f 2-4,11- data/VI-sequences_linear_blasthits-v1.tab | head -n 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some good blast hit's as well, let's put these next to the first 20 hmmscan hits I found before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(base) Azfi_s0076.g037908         9e-95  304.9 \n",
      "Azfi_s0021.g015882       4.5e-51  161.0 \n",
      "Azfi_s0041.g026722       4.3e-50  157.8 \n",
      "Azfi_s0004.g008455       1.9e-49  155.6 \n",
      "Azfi_s0035.g025508       4.2e-48  151.2 \n",
      "Azfi_s0010.g012145       1.7e-47  149.2 \n",
      "Azfi_s0008.g011612       6.6e-47  147.3 \n",
      "Azfi_s0129.g048859       8.1e-47  147.0 \n",
      "Azfi_s0005.g009119         5e-46  144.4 \n",
      "Azfi_s0005.g009154       5.5e-46  144.2 \n",
      "Azfi_s0812.g087843       9.4e-46  143.5 \n",
      "Azfi_s0061.g034930       3.6e-45  141.6 \n",
      "Azfi_s0445.g070637       5.6e-45  140.9 \n",
      "Azfi_s0001.g000839       1.9e-44  139.2 \n",
      "Azfi_s0006.g009830       5.4e-44  137.7 \n",
      "Azfi_s0014.g013584         1e-42  133.5 \n",
      "Azfi_s0003.g007581       4.1e-42  131.5 \n",
      "Azfi_s0010.g012085       4.9e-42  131.2 \n",
      "Azfi_s0003.g007699       6.2e-42  130.9 \n",
      "Azfi_s0390.g067689       9.9e-42  130.2 \n",
      "(base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "#excuse this ugly visualisation, I don't have the patience\n",
    "grep -v '#'  data/VI-sequences_linear_hmm-v1_Azfihits.tab | cut -c 52-70,80-100 | sort -k3nr | head -n 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It makes sense these aren't exactly the same, in the hmm I used more sequences while in blast I only used Salvinia sequences.\n",
    "I was planning to take the top 5 hmm hits to investigate further, but I'll take the top 5 blast hits too.\n",
    "This mostly overlaps anyway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "sort <(grep -v '#'  data/VI-sequences_linear_hmm-v1_Azfihits.tab | cut -c 52-70,80-100 | sort -k3nr | head -n 5 | cut -c 1-18 ) \\\n",
    "     <(cut -f 2 data/VI-sequences_linear_blasthits-v1.tab | head -n 5) \\\n",
    "     | uniq > data/Azfi-mybs-subfamVI-suspects.list\n",
    "     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azfi_s0004.g008455\n",
      "Azfi_s0014.g013584\n",
      "Azfi_s0021.g015882\n",
      "Azfi_s0035.g025508\n",
      "Azfi_s0041.g026722\n",
      "Azfi_s0076.g037908\n",
      "Azfi_s0129.g048859\n"
     ]
    }
   ],
   "source": [
    "cat data/Azfi-mybs-subfamVI-suspects.list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">\u001b[01;31m\u001b[KAzfi\u001b[m\u001b[K_s0004.g008455 Myb transcription factor [0.077]\n",
      ">\u001b[01;31m\u001b[KAzfi\u001b[m\u001b[K_s0021.g015882 Myb transcription factor [0.077]\n"
     ]
    }
   ],
   "source": [
    "grep Azfi ./data/combi_sequences_linear.fasta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not unimportant, the two Azfi type VII sequences I identified in notebook 2 are also in these blast hits. Including our best hit, so it may be al type VII perhaps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract sequences and compare to VI and VII seqeunces"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's linearise the annotation protein fasta.\n",
    "\n",
    "Then extract the Azfi sequences of interest and put these in a combi fasta with the previously examined VI and VII sequences.\n",
    "\n",
    "Finally, I'll make an alignment and log a snapshot here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "cat data/genome/Azolla_filiculoides.protein.highconfidence_v1.1.fasta \\\n",
    "     | awk '/^>/ {printf(\"%s%s\\n\",(N>0?\"\\n\":\"\"),$0);N++;next;} {printf(\"%s\",$0);} END {printf(\"\\n\");}' \\\n",
    "     > data/genome/Azolla_filiculoides.protein.highconfidence_v1.1_linear.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "grep -A 1 --no-group-separator \\\n",
    "     -f ./data/Azfi-mybs-subfamVI-suspects.list \\\n",
    "     ./data/genome/Azolla_filiculoides.protein.highconfidence_v1.1_linear.fasta \\\n",
    "     > ./data/Azfi-mybs-subfamVI-suspects.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "cat data/VI_sequences_linear.fasta \\\n",
    "    data/Azfi-mybs-subfamVI-suspects.fasta \\\n",
    "    data/VII_sequences_linear.fasta \\\n",
    "    > data/combi-VI-VII-Azfisuspects.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "inseq=combi-VI-VII-Azfisuspects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(phylogenetics) (phylogenetics) "
     ]
    }
   ],
   "source": [
    "conda activate phylogenetics\n",
    "linsi --thread $(nproc) data/$inseq.fasta > ./data/alignments_raw/\"$inseq\"_aligned-mafft-linsi.fasta \\\n",
    "                                                      2> ./data/alignments_raw/\"$inseq\"_aligned-mafft-linsi.log\n",
    "conda deactivate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outputhat23=16\n",
      "treein = 0\n",
      "compacttree = 0\n",
      "stacksize: 8192 kb\n",
      "All-to-all alignment.\n",
      "tbfast-pair (aa) Version 7.407\n",
      "alg=L, model=BLOSUM62, 2.00, -0.10, +0.10, noshift, amax=0.0\n",
      "4 thread(s)\n",
      "\n",
      "outputhat23=16\n",
      "Loading 'hat3.seed' ... \n",
      "done.\n",
      "Writing hat3 for iterative refinement\n",
      "Gap Penalty = -1.53, +0.00, +0.00\n",
      "tbutree = 1, compacttree = 0\n",
      "Constructing a UPGMA tree ... \n",
      "   20 / 29\n",
      "done.\n",
      "\n",
      "Progressive alignment ... \n",
      "STEP    28 /28 (thread    3) \n",
      "done.\n",
      "tbfast (aa) Version 7.407\n",
      "alg=A, model=BLOSUM62, 1.53, -0.00, -0.00, noshift, amax=0.0\n",
      "4 thread(s)\n",
      "\n",
      "minimumweight = 0.000010\n",
      "autosubalignment = 0.000000\n",
      "nthread = 4\n",
      "randomseed = 0\n",
      "blosum 62 / kimura 200\n",
      "poffset = 0\n",
      "niter = 16\n",
      "sueff_global = 0.100000\n",
      "nadd = 16\n",
      "Loading 'hat3' ... done.\n",
      "\n",
      "   20 / 29\n",
      "Segment   1/  1    1-2133\n",
      "006-0054-1 (thread    3) worse         \n",
      "Converged.\n",
      "done\n",
      "dvtditr (aa) Version 7.407\n",
      "alg=A, model=BLOSUM62, 1.53, -0.00, -0.00, noshift, amax=0.0\n",
      "4 thread(s)\n",
      "\n",
      "\n",
      "Strategy:\n",
      " L-INS-i (Probably most accurate, very slow)\n",
      " Iterative refinement method (<16) with LOCAL pairwise alignment information\n",
      "\n",
      "If unsure which option to use, try 'mafft --auto input > output'.\n",
      "For more information, see 'mafft --help', 'mafft --man' and the mafft page.\n",
      "\n",
      "The default gap scoring scheme has been changed in version 7.110 (2013 Oct).\n",
      "It tends to insert more gaps into gap-rich regions than previous versions.\n",
      "To disable this change, add the --leavegappyregion option.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cat ./data/alignments_raw/\"$inseq\"_aligned-mafft-linsi.log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(jalview) (jalview) "
     ]
    }
   ],
   "source": [
    "conda activate jalview\n",
    "for   i in data/alignments_raw/*aligned*.fasta\n",
    "do    prefix=$(echo $i | sed 's/\\.fasta//')\n",
    "      if   [ ! -f $prefix.png ]\n",
    "      then jalview -nodisplay \\\n",
    "              -open $prefix.fasta \\\n",
    "              -colour CLUSTAL \\\n",
    "              -png  $prefix.png > /dev/null 2> /dev/null\n",
    "      fi\n",
    "done\n",
    "conda deactivate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "of course the full aligment is super long:\n",
    "![alignment](./data/alignments_raw/combi-VI-VII-Azfisuspects_aligned-mafft-linsi.png)\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### intermediate conclusions\n",
    "While most Azfi sequences look like VII sequences (just as the two RNA seq targets identified before),\n",
    "it does seem I found a type VI one: Azfi_s0076.g037908\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trimming and phylogeny \n",
    "\n",
    "To back up my observation of this Type VI myb in Azfi, let's make a quick phylogenetic tree of this alignment after trimming much like in step 2.\n",
    "\n",
    "I may add more submafilies to this tree inference later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(phylogenetics) (phylogenetics) (phylogenetics) (phylogenetics) (phylogenetics) (phylogenetics) trimming alignment data/alignments_raw/combi-VI-VII-Azfisuspects_aligned-mafft-linsi.fasta\n",
      "(phylogenetics) (base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "conda activate phylogenetics\n",
    "if    [ ! -d data/alignments_trimmed ]\n",
    "then  mkdir  data/alignments_trimmed \n",
    "fi\n",
    "\n",
    "# define appendix only once here:\n",
    "trimappendix='trim-gt4'\n",
    "\n",
    "for a in \"data/alignments_raw/$inseq\"_aligned*.fasta\n",
    "do  appendix=$(echo $a | cut -d '/' -f 3- | sed \"s/$inseq\\_//\" | sed \"s/.fasta//\")\n",
    "    if    [ ! -f data/alignments_trimmed/\"$inseq\"_\"$appendix\"_\"$trimappendix\".fasta ]\n",
    "    then  echo \"trimming alignment $a\"\n",
    "          sed -i 's/ /_/g' $a\n",
    "          trimal -in $a   \\\n",
    "                 -out data/alignments_trimmed/\"$inseq\"_\"$appendix\"_\"$trimappendix\".fasta \\\n",
    "                 -gt .4 \\\n",
    "                 -htmlout data/alignments_trimmed/\"$inseq\"_\"$appendix\"_\"$trimappendix\".html\n",
    "    fi\n",
    "done\n",
    "conda deactivate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(jalview) (jalview) (base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "conda activate jalview\n",
    "for   i in data/alignments_trimmed/*.fasta\n",
    "do    prefix=$(echo $i | sed 's/\\.fasta//')\n",
    "      if    [ ! -f $prefix.png ]\n",
    "      then  jalview -nodisplay \\\n",
    "                    -open $prefix.fasta \\\n",
    "                    -colour CLUSTAL \\\n",
    "                    -png  $prefix.png > /dev/null 2> /dev/null\n",
    "      fi\n",
    "done\n",
    "conda deactivate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;34m./data/alignments_trimmed\u001b[00m\n",
      "├── combi_sequences_linear_aligned-mafft-linsi_trim-gt4.fasta\n",
      "├── \u001b[01;35mcombi_sequences_linear_aligned-mafft-linsi_trim-gt4.png\u001b[00m\n",
      "├── combi_sequences_linear_aligned-mafft-linsi_trim-gt6.fasta\n",
      "├── \u001b[01;35mcombi_sequences_linear_aligned-mafft-linsi_trim-gt6.png\u001b[00m\n",
      "├── combi-VI-VII-Azfisuspects_aligned-mafft-linsi_trim-gt4.fasta\n",
      "├── combi-VI-VII-Azfisuspects_aligned-mafft-linsi_trim-gt4.html\n",
      "└── \u001b[01;35mcombi-VI-VII-Azfisuspects_aligned-mafft-linsi_trim-gt4.png\u001b[00m\n",
      "\n",
      "0 directories, 7 files\n",
      "(base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "tree ./data/alignments_trimmed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's have a look at this trimmed alignment:\n",
    "![trim](data/alignments_trimmed/combi-VI-VII-Azfisuspects_aligned-mafft-linsi_trim-gt4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still looks quite lenient to me, but that's what I did in step two so I'll keep this for now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(phylogenetics) making a tree of file data/alignments_trimmed/combi-VI-VII-Azfisuspects_aligned-mafft-linsi_trim-gt4.fasta\n",
      "The first lines of alignment data/alignments_trimmed/combi-VI-VII-Azfisuspects_aligned-mafft-linsi_trim-gt4.fasta look like this\n",
      ">g8575.t1_length\n",
      "MDSGGQAHGQKVAAQQQSVGGGKGGGAVGPKRPLKRGVWLPEEDEILKGYVANQGPKNWS\n",
      "SIETMGLLARSGKSCRLRWMNHLRPDLNRSTKFTPEEEAIVVTKQKLHGNKWAQIAKSLS\n",
      "GRTDNDVKNFWNMRMKKLAKL--------ARLEKQRQRQLSPIAMAAAAMGLPPGARMLA\n",
      "SPGDVVVLQNFCSPSPKGYVGSHHHHHPHHLSPLSPTNRSGALSAALQASPCTRPGSVLT\n",
      "GVGRTTDPNMSSCNSTELNGQGAMAPSVARNASSSSPSHRHQQQQSVEGAVRSAVNGANQ\n",
      "ATTMSAPLRGVLNRSPSADMAATTSSSGGTGTTLMVGTSRQQRSQADDNAFAMEATAAID\n",
      "LLLEQQQRQTTMRQFGGLAGMGCAAATEEAEEEDDESGFLSGPPVTLPSHDPRQQQQQHP\n",
      "DQDTAASDAAYGSTLQMPTTVGGFSGQEQSSLLQTNGMRDATPPLGAAVPPLNDESNRLG\n",
      "SLSWAQDTVFLDSMLDSC\n",
      "Making a directory aligned-mafft-linsi_trim-gt4 to store trees (name based on alignment filename)\n",
      "(phylogenetics) (base) "
     ]
    },
    {
     "ename": "",
     "evalue": "1",
     "output_type": "error",
     "traceback": []
    }
   ],
   "source": [
    "conda activate phylogenetics\n",
    "for a in data/alignments_trimmed/\"$inseq\"_aligned*gt[46].fasta\n",
    "do  #iqpendix='iqtree-b100'\n",
    "    iqpendix='iqtree-bb2000-alrt2000'\n",
    "\n",
    "    echo \"making a tree of file $a\"\n",
    "    echo \"The first lines of alignment $a look like this\"\n",
    "    head $a\n",
    "\n",
    "    file_appendix=$(echo $a | cut -d '/' -f 3- | sed \"s/$inseq\\_//\" | sed \"s/.fasta//\")\n",
    "    \n",
    "    if   [ ! -d    analyses/\"$inseq\"_trees/\"$file_appendix\" ]\n",
    "    then echo \"Making a directory $file_appendix to store trees (name based on alignment filename)\"\n",
    "         mkdir -p  analyses/\"$inseq\"_trees/\"$file_appendix\" \n",
    "    fi\n",
    "\n",
    "    iqprefix=analyses/\"$inseq\"_trees/\"$file_appendix\"/\"$inseq\"_\"$file_appendix\"_\"$iqpendix\"\n",
    "    if   [ ! -f \"$iqprefix\".tree ]\n",
    "    then nice iqtree -s $a \\\n",
    "                     -m MFP \\\n",
    "                     -bb 2000 -alrt 2000 \\\n",
    "                     -nt AUTO \\\n",
    "                     -ntmax $(nproc)  \\\n",
    "                     -pre  \"$iqprefix\" \\\n",
    "                     2>   \"$iqprefix\".stderr \\\n",
    "                      >    \"$iqprefix\".stdout\n",
    "    #cat \"$iqprefix\".log | mail -s \"IQtree_run $a\" laura\n",
    "    fi\n",
    "done\n",
    "conda deactivate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initial tree supports inspection of alignment, g037908 seems to be a type VI MYB that wasn't picked up before.\n",
    "\n",
    "It seems g008455 has a salivianels duplication variant g015882.\n",
    "\n",
    "Then there is four other genes which may be duplication results of two original genes. It could be interesting to put those into a category as well and inspect their expression.\n",
    "\n",
    "tree: https://itol.embl.de/tree/9421021579392231596463872\n",
    "\n",
    "![tree image](analyses/combi-VI-VII-Azfisuspects_trees/aligned-mafft-linsi_trim-gt4/combi-VI-VII-Azfisuspects_aligned-mafft-linsi_trim-gt4_iqtree-bb2000-alrt2000.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's do a propper support tree as well, just in case..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best-fit model according to BIC: JTTDCMut+F+I+G4\n"
     ]
    }
   ],
   "source": [
    "grep 'Best-fit model' analyses/combi-VI-VII-Azfisuspects_trees/aligned-mafft-linsi_trim-gt4/combi-VI-VII-Azfisuspects_aligned-mafft-linsi_trim-gt4_iqtree-bb2000-alrt2000.iqtree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(phylogenetics) making a tree of file data/alignments_trimmed/combi-VI-VII-Azfisuspects_aligned-mafft-linsi_trim-gt4.fasta\n",
      "The first lines of alignment data/alignments_trimmed/combi-VI-VII-Azfisuspects_aligned-mafft-linsi_trim-gt4.fasta look like this\n",
      ">g8575.t1_length\n",
      "MDSGGQAHGQKVAAQQQSVGGGKGGGAVGPKRPLKRGVWLPEEDEILKGYVANQGPKNWS\n",
      "SIETMGLLARSGKSCRLRWMNHLRPDLNRSTKFTPEEEAIVVTKQKLHGNKWAQIAKSLS\n",
      "GRTDNDVKNFWNMRMKKLAKL--------ARLEKQRQRQLSPIAMAAAAMGLPPGARMLA\n",
      "SPGDVVVLQNFCSPSPKGYVGSHHHHHPHHLSPLSPTNRSGALSAALQASPCTRPGSVLT\n",
      "GVGRTTDPNMSSCNSTELNGQGAMAPSVARNASSSSPSHRHQQQQSVEGAVRSAVNGANQ\n",
      "ATTMSAPLRGVLNRSPSADMAATTSSSGGTGTTLMVGTSRQQRSQADDNAFAMEATAAID\n",
      "LLLEQQQRQTTMRQFGGLAGMGCAAATEEAEEEDDESGFLSGPPVTLPSHDPRQQQQQHP\n",
      "DQDTAASDAAYGSTLQMPTTVGGFSGQEQSSLLQTNGMRDATPPLGAAVPPLNDESNRLG\n",
      "SLSWAQDTVFLDSMLDSC\n"
     ]
    }
   ],
   "source": [
    "conda activate phylogenetics\n",
    "for a in data/alignments_trimmed/\"$inseq\"_aligned*gt[46].fasta\n",
    "do  iqpendix='iqtree-b200'\n",
    "    #iqpendix='iqtree-bb2000-alrt2000'\n",
    "\n",
    "    echo \"making a tree of file $a\"\n",
    "    echo \"The first lines of alignment $a look like this\"\n",
    "    head $a\n",
    "\n",
    "    file_appendix=$(echo $a | cut -d '/' -f 3- | sed \"s/$inseq\\_//\" | sed \"s/.fasta//\")\n",
    "    \n",
    "    if   [ ! -d    analyses/\"$inseq\"_trees/\"$file_appendix\" ]\n",
    "    then echo \"Making a directory $file_appendix to store trees (name based on alignment filename)\"\n",
    "         mkdir -p  analyses/\"$inseq\"_trees/\"$file_appendix\" \n",
    "    fi\n",
    "\n",
    "    iqprefix=analyses/\"$inseq\"_trees/\"$file_appendix\"/\"$inseq\"_\"$file_appendix\"_\"$iqpendix\"\n",
    "    if   [ ! -f \"$iqprefix\".tree ]\n",
    "    then nice iqtree -s $a \\\n",
    "                     -m 'JTTDCMut+F+I+G4' \\\n",
    "                     -b 200 \\\n",
    "                     -nt AUTO \\\n",
    "                     -ntmax $(nproc)  \\\n",
    "                     -pre  \"$iqprefix\" \\\n",
    "                     2>   \"$iqprefix\".stderr \\\n",
    "                      >    \"$iqprefix\".stdout\n",
    "    cat \"$iqprefix\".log | mail -s \"IQtree_run $a\" laura\n",
    "    fi\n",
    "done\n",
    "conda deactivate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# conclusion\n",
    "\n",
    "The [nonparametric tree](https://itol.embl.de/tree/1312115964265771596529418) confirms the results of the preliminary tree and the MSA. \n",
    "Sequence `Azfi_s0076.g037908` is a type VI R2R3 MYB. \n",
    "I found this sequence (and a couple of others) with an HMM model of the R&J Type VI sequences; which I then used to scan all known _Azolla filiculoides_ transcripts on fernbase.org.\n",
    "\n",
    "Next steps: \n",
    "* Can I add a subfamily label to the other Azfi sequences\n",
    "* Finally, I'd like to log2 expressen/fold change to the Azfi sequences; to illustrate how these subfamilies change expression upon transistion to sexual reproduction at least in _Azolla_\n",
    "\n",
    "This notebook addressed one of tree open questions that were raised by notebook 2.\n",
    "The two still remaining are:\n",
    "* Can I infer the phylogeny to more confidently/explicitly draw the Salvinales VII branch as a part of the remaining VII branch by including other subfamiles and an outgroup\n",
    "   - Hopefully, I can tackle this and the first point in 'next steps' simultaneously.\n",
    "* Can I add intron/exon structures as a second line of evidence supporting the different subfamilies\n",
    "\n",
    "And then there was some superficial stuff that will come when I have all the data ready.\n"
   ]
  }
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