Mm9 multiple alignment

See also:

• all other UCSC Multiple Alignments

Mouse mm9 multiple alignment/conservation track

To avoid artifacts in downstream processing of the UCSC multiple alignments, it is important to be careful on the use of the parameters used in the blastz processing pipeline. There are a number of steps in the pipeline and a variety of tunable parameters involved. This page will track the various parameters used in the alignments as they proceed toward the completion of a multiple alignment conservation track on the mm9 mouse (NCBI build 37) assembly The chrom count in the table below does not include haplotypes, chr*_random, chrUn or chrM unless chrUn or scaffolds are the only sequences for that assembly. The genome size has the same limitation as the chrom count, no randoms. Tree distances are from the hg18 28-way measurements, with ponAbe2 and calJac1 manually inserted into the tree. 1 Mb = 1000 * 1000 = 1,000,000 bases Types of mafs used in the multiple alignment: 2X mammal assemblies == reciprocal best net calJac1, cavPor2, tupBel1, otoGar1, dasNov1, oryCun1, felCat3, loxAfr1, eriEur1, sorAra1, echTel1 high coverage placental mammals == syntenic net rn4, hg18, rheMac2, ponAbe2, panTro2, equCab1, canFam2, bosTau3 all others == standard net monDom4, ornAna1, galGal3, anoCar1, xenTro2, gasAcu1, danRer5, tetNig1, fr2, oryLat1

30-way phylogenetic tree

axtChain parameters and end results

name db	chrom count (*)	genome size	tree distance	axtChain minScore	axtChain linearGap	% of mm9 matched	% of other matched by mm9	done
mouse mm9 self alignment	21	2654 Mb	0.0	10000	medium	14.458	N/A	2007-08-31
rat rn4	21	2718 Mb	0.160657	5000	medium	65.380	66.538	2007-08-31
human hg18	24	3080 Mb	0.452619	3000	medium	38.499	35.201	2007-08-16
Rhesus rheMac2	22	2864 Mb	0.452745	3000	medium	38.087	41.335	2007-09-07
Orangutan ponAbe2	25	3029 Mb	0.453809	3000	medium	34.902	30.659	2007-09-21
Marmoset calJac1	49724	3029 Mb	0.454272	3000	medium	32.971	30.302	2007-09-07
Chimp panTro2	25	3175 Mb	0.454514	3000	medium	37.674	34.269	2007-09-25
GuineaPig cavPor2	295514	3403 Mb	0.479871	3000	medium	18.326	N/A	2007-09-21
Horse equCab1	32	2056 Mb	0.479871	3000	medium	34.782	37.217	2007-09-25
TreeShrew tupBel1	150851	3660 Mb	0.494934	3000	medium	21.099	N/A	2007-10-01
Bushbaby otoGar1	120882	3420 Mb	0.498957	3000	medium	22.972	N/A	2007-09-28
Armadillo dasNov1	304391	3856 Mb	0.517360	3000	medium	16.547	N/A	2007-10-02
Rabbit oryCun1	215471	3464 Mb	0.519779	3000	medium	18.945	N/A	2007-09-29
Cat felCat3	217790	4045 Mb	0.530610	3000	medium	19.077	N/A	2007-09-29
Dog canFam2	39	2445 Mb	0.533544	3000	medium	32.362	34.891	2007-09-05
Elephant loxAfr1	233134	3707 Mb	0.536627	3000	medium	18.052	N/A	2007-10-01
Cow bosTau3	30	2434 Mb	0.540852	3000	medium	26.352	25.909	2007-09-25
Hedgehog eriEur1	379801	3367 Mb	0.632457	3000	medium	10.022	N/A	2007-10-02
Shrew sorAra1	262057	2936 Mb	0.658734	3000	medium	9.556	N/A	2007-10-01
Tenrec echTel1	325491	3823 Mb	0.666303	3000	medium	11.141	14.144	2007-09-27
Opossum monDom4	9	3431 Mb	0.909852	5000	loose	9.752	7.254	2007-09-27
Platypus ornAna1	201522	1996 Mb	1.165888	5000	loose	5.417	7.359	2007-09-25
Chicken galGal3	33	1032 Mb	1.285399	5000	loose	3.729	8.152	2007-09-25
Lizard anoCar1	7233	1781 Mb	1.404225	5000	loose	3.406	4.934	2007-09-21
X. tropicalis xenTro2	19759	1513 Mb	1.726205	5000	loose	3.131	2.651	2007-09-24
Stickleback gasAcu1	21	400 Mb	2.012649	5000	loose	1.849	9.791	2007-09-07
Zebrafish danRer5	25	1547 Mb	2.027153	5000	loose	3.255	4.625	2007-09-13
Tetraodon tetNig1	21	217 Mb	2.051015	5000	loose	1.763	12.341	2007-09-07
Fugu fr2	1	400 Mb	2.086669	5000	loose	1.794	10.784	2007-09-07
Medaka oryLat1	24	724 Mb	2.200402	5000	loose	1.933	6.495	2007-08-31

(*) chrom count does not include haplotypes, chr*_random, chrUn or chrM unless chrUn or scaffolds are the only sequences for that assembly.

The genome size has the same limitation as the chrom count, no randoms.

Tree distances are from the hg18 28-way measurements, with ponAbe1 and calJac1 manually inserted into the tree.

blastz alignment parameters details

One special run, Rat rn4:

M=40M, K=4500, L=4500, Y=15000, T=2 O=600, Q=mouse_rat.q E=55

query	abridged repeats	M	K	L	Q	Y
Rat rn4	yes	40M	4500	4500	mouse-rat	15000
Human hg18	yes	40M	3K	3K	default	9400
Rhesus rheMac2	yes	40M	3K	3K	default	9400
Orangutan ponAbe2	no	50	3K	3K	default	9400
Marmoset calJac1	no	50	3K	3K	default	9400
Chimp panTro2	yes	40M	3K	3K	default	9400
GuineaPig cavPor2	no	50	3K	3K	default	9400
Horse equCab1	no	40M	3K	3K	default	9400
TreeShrew tupBel1	no	50	3K	3K	default	9400
Bushbaby otoGar1	no	50	3K	3K	default	9400
Armadillo dasNov1	no	50	3K	3K	default	9400
Rabbit oryCun1	no	50	3K	3K	default	9400
Cat felCat3	no	50	3K	3K	default	9400
Dog canFam2	yes	40M	3K	3K	default	9400
Elephant loxAfr1	no	50	3K	3K	default	9400
Cow bosTau3	no	40M	3K	3K	default	9400
Hedgehog eriEur1	no	50	3K	3K	default	9400
Shrew sorAra1	no	50	3K	3K	default	9400
Tenrec echTel1	no	50	3K	3K	default	9400
Opossum monDom4	no	50	2200	6000	HoxD55	3400
Platypus ornAna1	no	50	2200	6000	HoxD55	3400
Chicken galGal3	yes	40M	2200	6000	HoxD55	3400
Lizard anoCar1	no	50	2200	6000	HoxD55	3400
X_tropicalis xenTro2	no	50	2200	6000	HoxD55	3400
Stickleback gasAcu1	no	50	2200	6000	HoxD55	3400
Zebrafish danRer5	no	50	2200	6000	HoxD55	3400
Tetraodon tetNig1	no	50	2200	6000	HoxD55	3400
Fugu fr2	no	50	2200	6000	HoxD55	3400
Medaka oryLat1	no	50	2200	6000	HoxD55	3400

default blastz parameters

m=80  v=0  B=2  C=0  E=30  G=0  H=0  K=3000 L=K
M=0 O=400 P=1 R=0 T=1 W=8 X=10*(A-to-A match score)
Y=O+300*E Z=1

From the blastz usage message:

Default values are given in parentheses.
  m(80M) bytes of space for trace-back information
  v(0) 0: quiet; 1: verbose progress reports to stderr
  B(2) 0: single strand; >0: both strands
  C(0) 0: no chaining; 1: just output chain; 2: chain and extend;
       3: just output HSPs
  E(30) gap-extension penalty.
  G(0) diagonal chaining penalty.
  H(0) interpolate between alignments at threshold K = argument.
  K(3000) threshold for MSPs
  L(K) threshold for gapped alignments
  M(0) mask any base in seq1 hit this many times; 0 = no dynamic masking
  O(400) gap-open penalty.
  P(1) 0: entropy not used; 1: entropy used; >1 entropy with feedback.
  Q load the scoring matrix from a file.
  R(0) antidiagonal chaining penalty.
  T(1) 0: W-bp words;  1: 12of19;  2: 12of19 without transitions.
                       3: 14of22;  4: 14of22 without transitions.
  W(8) word size (unused unless T=0)
  X(10*(A-to-A match score)) X-drop parameter for ungapped extension.
  Y(O+300E) X-drop parameter for gapped extension.
  Z(1) increment between successive words in sequence 1.

The default scoring matrix is:		The HoxD55 scoring matrix is:		The mouse-rat scoring matrix is:
A C G T A 91 -114 -31 -123 C -114 100 -125 -31 G -31 -125 100 -114 T -123 -31 -114 91 O=400 E=30		A C G T A 91 -90 -25 -100 C -90 100 -100 -25 G -25 -100 100 -90 T -100 -25 -90 91 O=400 E=30		A C G T A 56 -109 -45 -137 C -109 100 -103 -45 G -45 -103 100 -109 T -137 -45 -109 56 O=600 E=55

Additional doBlastzChainNet.pl parameters to tune

These parameters for blastz are placed in the DEF file used by the doBlastzChainNet.pl perl script. There are some additional parameters worth discussing:

• SEQ1* variables describe the target/reference assembly, while SEQ2* variables describe the query assembly.
• SEQn_CHUNK and SEQn_LAP determine the step size and overlap size of chunks into which large target or query sequences are to be split before alignment. SEQ1_LAP is always set to 10000 for reasons described in the blastz paper, while SEQ2_LAP is traditionaly set to 0. Since the CHUNK size will determine how a genome is partitioned, smaller CHUNKS result in more blastz jobs and larger CHUNKS result in fewer jobs which will take longer to run.
• SEQn_LIMIT decides what the maximum number of sequences should be for any partitioned file. This limit only effects SEQ1 or SEQ2 when they are 2bit files. Some 2bit files have too many contigs which will result in blastz hippos (jobs taking forever compared to the other jobs). SEQ1_LIMIT defaults to 30 and SEQ2_LIMIT defaults to 100. By setting a lower limit, hippos may be avoided but more jobs will result. By setting this limit higher, fewer but longer running jobs may result. Apologies to Hippopotamus amphibius.
• The specifics of the target and query genomes may result in very different blastz run sizes, but CHUNK and LIMIT can be used to tune the size of the blastz run. One means of tuning is to run doBlastzChainNet.pl with the -stop=partition flag to test the results of the partition step until satisfied, then continue with the -continue=blastz. To determine the number of blastz jobs that will result from a given partition, multiply the number of lines in {target}.lst by the number of lines in the {query}.lst which will be found in the run.blastz directory. Expect a tuned blastz run for two 3Mb genomes will have in 50K-100K jobs in the blastz batch.
• While tuning the number of jobs is easy, what the goal should be is to tune the time that a job runs. Angie recommends an *average* job time to be ~3-7 minutes, no less than 45s and no greater than 20 minutes. Jim probably favors smaller numbers for each of those.

axtChain matrix parameters

The "medium" gap score matrix, tuned for the mouse-human distance is:

tableSize    11
smallSize   111
position  1   2   3   11  111  2111  12111  32111   72111  152111  252111
qGap    350 425 450  600  900  2900  22900  57900  117900  217900  317900
tGap    350 425 450  600  900  2900  22900  57900  117900  217900  317900
bothGap 750 825 850 1000 1300  3300  23300  58300  118300  218300  318300

The "loose" gap score matrix, tuned for the chicken-human distance is:

tablesize    11
smallSize   111
position  1   2   3   11  111  2111  12111  32111  72111  152111  252111
qGap    325 360 400  450  600  1100   3600   7600  15600   31600   56600
tGap    325 360 400  450  600  1100   3600   7600  15600   31600   56600
bothGap 625 660 700  750  900  1400   4000   8000  16000   32000   57000

the tree diagram

Created from the Hg18 28-way diagram, with the two new species, ponAbe2 and calJac1 manually inserted:

((((((((

 (((Mouse_mm9:0.076274,Rat_rn4:0.084383):0.200607,
    GuineaPig_cavPor2:0.202990):0.034350,
        Rabbit_oryCun1:0.208548):0.014587,

((((((Human_hg18:0.005873,Chimp_panTro2:0.007668):0.013037,
   Orangutan_ponAbe2:0.02):0.013037,Rhesus_rheMac2:0.031973):0.0365,
        Marmoset_calJac1:0.07):0.0365,Bushbaby_otoGar1:0.151185):0.015682,
           TreeShrew_tupBel1:0.162844):0.006272):0.019763,

 ((Shrew_sorAra1:0.248532,Hedgehog_eriEur1:0.222255):0.045693,

 (((Dog_canFam2:0.101137,Cat_felCat3:0.098203):0.048213,
    Horse_equCab1:0.099323):0.007287,
        Cow_bosTau3:0.163945):0.012398):0.018928):0.030081,

 (Armadillo_dasNov1:0.133274,(Elephant_loxAfr1:0.103030,
        Tenrec_echTel1:0.232706):0.049511):0.008424):0.213469,

 Opossum_monDom4:0.320721):0.088647,
    Platypus_ornAna1:0.488110):0.118797,
        (Chicken_galGal3:0.395136,Lizard_anoCar1:0.513962):0.093688):0.151358,
            Frog_xenTro2:0.778272):0.174596,

 (((Tetraodon_tetNig1:0.203933,Fugu_fr2:0.239587):0.203949,
    (Stickleback_gasAcu1:0.314162,Medaka_oryLat1:0.501915):0.055354):0.346008,
Zebrafish_danRer5:0.730028):0.174596);

phastCons graph procedure

The maf files from the 30-way alignment are split for phastCons with msa_split and the following parameters:

msa_split <eachChrom>.maf -i MAF -M mm9.thisChr.fa -o SS -w 10000000,0 -I 1000 -B 5000

An attempt was made to run phyloFit on some of the smaller chromosome .ss files, but this procedure consumes too much CPU time and did not give a good result for a starting-tree to phastCons. Instead, using the starting-tree from the Hg18 28-way phastCons operation, insert the two new organisms into the tree diagram, with similar distances as seen in the failed attempt of using phyloFit on several small chromosomes, to arrive at a starting-tree.mod file:

ALPHABET: A C G T 
ORDER: 0
SUBST_MOD: REV
BACKGROUND: 0.295000 0.205000 0.205000 0.295000 
RATE_MAT:
  -0.990634    0.178819    0.490738    0.321078 
   0.257325   -1.001677    0.186563    0.557790 
   0.706184    0.186563   -1.161873    0.269126 
   0.321078    0.387615    0.187019   -0.895713 
TREE: (((((((((((mm9:0.076274,rn4:0.084383):0.200607,cavPor2:0.202990):0.034350,
oryCun1:0.208548):0.014587,((((((hg18:0.005873,panTro2:0.007668):0.013037,
ponAbe2:0.019969):0.013037,rheMac2:0.031973):0.013300,calJac1:0.074420):0.068818,
otoGar1:0.151185):0.015682,tupBel1:0.162844):0.006272):0.019763,
((sorAra1:0.248532,eriEur1:0.222255):0.045693,
(((canFam2:0.101137,felCat3:0.098203):0.048213,equCab1:0.099323):0.007287,
bosTau3:0.163945):0.012398):0.018928):0.030081,(dasNov1:0.133274,
(loxAfr1:0.103030,echTel1:0.232706):0.049511):0.008424):0.213469,
monDom4:0.320721):0.088647,ornAna1:0.488110):0.118797,
(galGal3:0.395136,anoCar1:0.513962):0.093688):0.151358,xenTro2:0.778272):0.174596,
(((tetNig1:0.203933,fr2:0.239587):0.203949,
(gasAcu1:0.314162,oryLat1:0.501915):0.055354):0.346008,danRer5:0.730028):0.174596);

Then, running phastCons on each .ss file with the parameters:

phastCons <oneFile>.ss starting-tree.mod --rho .31 --expected-length 45
   --target-coverage .3 --quiet --seqname <chrName> --idpref <chrName>
   --most-conserved <oneFile>.bed --score

Combine all the most-conserved .bed files into a single mostConserved.bed file and add lod score calculation:

cat bed/*/chr*.bed | sort -k1,1 -k2,2n | \
        awk '{printf "%s\t%d\t%d\tlod=%d\t%s\n", $1, $2, $3, $5, $5;}' | \
            /cluster/bin/scripts/lodToBedScore /dev/stdin > mostConserved.bed

That result is loaded for the Most Conserved track in the table phastConsElements30way. A featureBits measurement verifies a target coverage of refGene CDS near 70%:

$  featureBits mm9 -enrichment refGene:cds phastConsElements30way
refGene:cds 1.167%, phastConsElements30way 5.841%, both 0.863%, cover 73.90%, enrich 12.65x

Encoding the wiggle data from the pp files from the phastCons run:

for D in pp/chr*
do
    ls $D/*.pp | sort -n -t\. -k2
done | xargs cat \
        | wigEncode -noOverlap stdin phastCons30way.wig phastCons30way.wib

The sort of the files names gets them into chrom position numerical order for input to the wigEncode. Any data input to wigEncode must be in chrom position numerical order for correct encoding.

alternative phastCons graphs

Two subsets of the organisms were used to compute alternative phastCons graphs.

1. Euarchontoglires - mm9,rn4,cavPor2,oryCun1,hg18,panTro2,ponAbe2, rheMac2,calJac1,otoGar1,tupBel1
2. Placentals - mm9,rn4,cavPor2,oryCun1,hg18,panTro2,ponAbe2, rheMac2,calJac1,otoGar1,tupBel1,sorAra1,eriEur1,canFam2,felCat3, equCab1,bosTau3,dasNov1,loxAfr1,echTel1

The same operating parameters were used during the phastCons operation.

The starting-tree.mod file for each of these was created from the overall 30-way starting-tree.mod file trimmed down with tree_doctor. Defining trees of:

Euarchontoglires:

TREE: ((((mm9:0.076274,rn4:0.084383):0.200607,cavPor2:0.202990):0.034350,
oryCun1:0.208548):0.014587,((((((hg18:0.005873,panTro2:0.007668):0.013037,
ponAbe2:0.019969):0.013037,rheMac2:0.031973):0.013300,calJac1:0.074420):0.068818,
otoGar1:0.151185):0.015682,tupBel1:0.162844):0.006272);

Placentals:

TREE: ((((((mm9:0.076274,rn4:0.084383):0.200607,cavPor2:0.202990):0.034350,
oryCun1:0.208548):0.014587,((((((hg18:0.005873,panTro2:0.007668):0.013037,
ponAbe2:0.019969):0.013037,rheMac2:0.031973):0.013300,calJac1:0.074420):0.068818,
otoGar1:0.151185):0.015682,tupBel1:0.162844):0.006272):0.019763,
((sorAra1:0.248532,eriEur1:0.222255):0.045693,
(((canFam2:0.101137,felCat3:0.098203):0.048213,equCab1:0.099323):0.007287,
bosTau3:0.163945):0.012398):0.018928):0.030081,(dasNov1:0.133274,
(loxAfr1:0.103030,echTel1:0.232706):0.049511):0.008424);

phastCons histograms

Histogram data is created from the loaded track data with the hgWiggle command:

$ hgWiggle -doHistogram -hBinSize=0.001 -hBinCount=1000 -hMinVal=0.0 -verbose=2 \
            -db=mm9 phastCons30way > histogram.data 2>&1

phastCons Histogram

Euarchontoglires subset:

Euarchontoglires phastCons Histogram

Placental subset:

Placental phastCons Histogram