zhaotao1987 / synnet-pipeline

Workflow for Building Microsynteny Networks

Microsynteny Network Construction and Analysis Pipeline

Please refer to Wiki for the up-to-date information.

 

 

NEWS July 30, 2022

For an easy use of the main functions of this synteny network construction and analysis pipeline, you are welcome to try the R package ‘syntenet’ which has been recently implemented, thanks to Fabrício Almeida-Silva et al.

With it, users can perform synteny detection (MCScanX algorithm implemented in R), network inference, infomap clustering, phylogenomic profiling, and synteny based phylogeny reconstruction as well (Zhao et al., 2021 Nat Commun). Everything in R.

Bioconductor - syntenet (development version)

almeidasilvaf/syntenet: An R package to infer and analyze synteny networks from protein sequences (github.com)

 

UPDATE Feb 19, 2021

Please refer to Wiki for an updated manual

 

UPDATE June 7, 2019

SynetBuild-X.sh

Improved the function enabling parallel running of MCScanX.

Codes are better organized and explained.

Four key parameters:  k (# tophits), s (# anchors), m (# gaps), and p (# CPUs)

Setting for -p is used for both Diamond and MCScanX parralleling. 

Notes:

Change Line70 according to your genome list

Options:

Line127: duplicate_gene_classifier

Line132: detect_collinear_tandem_arrays [intra-species]

Line180-183: detect_collinear_tandem_arrays [inter-species]

 

UPDATE June 3, 2019

Phylogenomic Profiling

When you have constructed a synteny network database of your interested genomes, you could then perform clustering to the entire network (using infomap algorithm for example), or you could filter out subnetworks first (for certain gene family) and then perform clustering (using infomap, CFinder etc.).

Next, we would like to summarize clusters according to its node compositions. Then we could infer what are the conserved clusters, and what are the specific ones (shared by certain species group for example). A rough description of this process is like this:  we first generate a matrix, rows stand for clusters, and columns stand for species, the value stands for the number of nodes of that species in that cluster. Then we calculate a distance matrix between pair-wise clusters, and finally perform hierarchical clustering to cluster similar-patterned clusters.

	Species 1	Species 2	Species 3	…	Species n
Cluster 1	1	2	1	…	1
…..	0	0	1	…	1
Cluster n	0	1	1	…	0

 

Now let’s start. Suppose you are using the infomap script infomap.r, the result looks like this:

names   mem

aar_AA31G00673  1

aar_AA32G00725  2

aar_AA39G00041  1

aar_AA29G00273  3

ach_Achn050361  1

ach_Achn168171  1

ach_Achn330591  1

ach_Achn198651  1

ach_Achn060901  1

…….

Result in such format can be directly feed into Phylogenomic_Profiling.r This R script can analyze cluster composition, calculate distance, and perform hierarchical clustering. Please read the notes within the codes. Also remember to change the content of Line 22 according to the genomes you are using.

Usage: Rscript Phylogenomic_Profiling.r infomap_clustering_result cluster_profiled cluster_profiled_clustered

 

UPDATE July 5, 2018

Here I attach two scripts, using DIAMOND (Buchfink et al., 2015) for faster genome comparisons at the first step.

SynetBuilding-Diamond.sh: Used for the first time, when you would like to construct synteny network of your interested genomes

SynetAdding-Diamond.sh: Used when you would like to add new genomes into the existing results.

-Prepeations

   - Whole genome protein files in fasta format.

   - GFF/BED file for each genome (Example: http://chibba.pgml.uga.edu/mcscan2/examples/at.gff)

   -Install DIAMOND and MCScanX (Wang et al., 2012) 

       -DIAMOND: https://github.com/bbuchfink/diamond

       -MCScanX:  http://chibba.pgml.uga.edu/mcscan2/

-Notes / Tips

   - Index your genome files, using 3-5 letters, for example for "Arabidopis thaliana", rename genome file and gff file as "Ath.pep" and "Ath.bed", and for "Oryza sativa" ("Osa.pep" and "Osa.bed")

-To run

  - Put all pairs of "*.pep" and "*.bed" of your genomes under one folder, copy the script (SynetBuilding-  Diamond.sh) to the same folder. 

  - Change the line 41 in the script (content in array): enter the genome indexes of your own selection. 

     - for example:      array=(ath osa oth Alyr) 

  - The array can be of any length, depending the genomes you want to compare. 

 

==========================================================================

 

Plant Phylogenomic Synteny Network Construction and Analysis Pipeline

Synteny network construction consists of five primary steps: (1) Annotated genome data preparation, (2) pairwise whole-genome comparisons, (3) syntenic block detection and data merging, (4) sub-network extraction (optional), and (5) network data analysis and visualization.

For Step 1, plant genomes can be downloaded from Phytozome, NCBI, Plaza, CoGe, etc. For each genome two files are needed: peptide sequences for all annotated/predicted genes (primary transcripts only) and a bed/GFF file indicating the genomic location of each gene. Users can prepare any number of genomes for synteny network construction. More genomes, longer computation time required.

>>> Fifty-one plant genomes used in the study of Tao Zhao et al., 2017b are listed and available for download below (Table 1).

For Steps 2 and 3, we provide a bash script (SynNet.sh) that can automatically perform pairwise inter- and intra- species comparisons, trimming the outputs for synteny detection, and treating outputs containing all synteny blocks to a final network file. This network database contains four columns: Block_ID, Block_Score, Gene1, and Gene2 (Gene 1 and Gene 2 are a syntenic gene pair).

• Users have to pre-install RAPSearch2 (BLAST-like program, but much faster) and MCScanX (for pairwise synteny block detection).
• Put all required genome files and the bash script in the same directory. Then, alter the first line of the program, which is a bracket containing species abbreviations (which are consistent to the names used in the genome files, tab separated).
• Run the program and get the result file called “Final_Network”, which contains all pairwise synteny blocks of your input species.

>>> Synteny network of the fifty-one plant genomes used in the study of Tao et al., 2017b are available for download (“51_Genomes_Blocks”).

At Step 4, for specific gene family studies you may need to extract sub-networks from the database. To do this, you need to first identify all gene family members from the genomes and then query this gene candidate list against the synteny block database.

• We use HMMER for gene family identification. HMMs (Hidden Markov Models) for specific gene families can be obtained from Pfam. Users can use Pfam Search or NCBI BLAST to help identify the feature domain(s) in the protein sequence. For example, a plant MADS-box protein is characterized by a core DNA binding domain (PF00319).

       Brief Guidelines of HMMER Usage:

• Install HMMER followed the instructions at: http://hmmer.org/documentation.html

• Download the protein sequence alignment for PF00319 in Stockholm format (default name : “PF00319_seed.txt”): http://pfam.xfam.org/family/PF00319#tabview=tab3

• Hmmbuild: to make a model from the alignment

  • Usage: hmmbuild [-options] <hmmfile output> <alignment file input>
  • Example: hmmbuild MADS.hmm PF00319_seed.txt
  • hmm is the output model for characterizing MADS-box genes.

• Hmmsearch: to identify all candidate members from the peptide database.

  • Usage: hmmsearch [options] <query hmmfile> <target seqfile>

  • Example: hmmsearch MADS.hmm 51_Genomes_Peps > MADS_Results

>>> Peptides for 51 plant genomes are merged and available for download, which can be used for an easier identification of gene family members of all 51 genomes.  (“51_Genomes_Peps”).

>>> The gene list of candidate MADS-box genes from the 51 Genomes (“MADS_list”)

• Extract subnetwork from the synteny network database as needed, using a list containing all HMMER-identified family members.
• Command: grep -f MADS_list 51_Genomes_Blocks > MADS.SynNet
• Now we obtain all syntenic relationships for all MADS-box genes.

>>> Synteny network of MADS-box genes across 51genomes (MADS.SynNet)

Step 5:

The subnetwork file (MADS.SynNet) can be trimmed into several formats for clustering and visualization, which can be performed in different ways.

Clustering algorithms: K-clique percolation (e.g., CFinder, SNAP), Infomap, CNM, k-core decomposition, etc.

Visualization tools:  Cytoscape, Gephi et al.

>>> Example networks from Tao Zhao et al., 2017b are available for download and visualization in Cytoscape (MADS.cys), Cytoscape version 3.4.0.

 

Table 1: Genomes Used in the study of Tao Zhao et al., 2017

No	Species	Order	Peptides	BED/GFF	Version	#Genes	Reference
1	Phaseolus vulgaris (Common bean)	Rosids	pv.pep	pv.bed	Version 1.0	27082	Schmutz et al., 2014
2	Glycine max (Soybean)	Rosids	gm.pep	gm.bed	Wm82.a2.v1	56044	Schmutz et al., 2010
3	Cajanus cajan (Pigeonpea)	Rosids	cc.pep	cc.bed	Nov_2011	48680	Varshney et al., 2012
4	Medicago truncatula (Barrel medic)	Rosids	mt.pep	mt.bed	Mt4.0v1	50894	Young et al., 2011
5	Cicer arietinum (Chickpea)	Rosids	ca.pep	ca.bed	Version 1.0	28269	Varshney et al., 2013
6	Lotus japonicus (Lotus)	Rosids	lj.pep	lj.bed	Version 2.5	42399	Sato et al., 2008
7	Citrullus lanatus (Watermelon)	Rosids	cl.pep	cl.bed	Version 1.0	23440	Guo et al., 2013
8	Cucumis sativus (Cucumber)	Rosids	cs.pep	cs.bed	Version 1.0	21491	Huang et al., 2009
9	Populus trichocarpa (Western poplar)	Rosids	pt.pep	pt.bed	Version 3.0	41335	Tuskan et al., 2006
10	Ricinus communis (Castor bean)	Rosids	rc.pep	rc.bed	Version 0.1	38613	Chan et al., 2010
11	Malus x domestica (Apple)	Rosids	md.pep	md.bed	Version 1.0	63514	Velasco et al., 2010
12	Pyrus x bretschneideri (Pear)	Rosids	pb.pep	pb.bed	Version 1.0	42812	Wu et al., 2013
13	Prunus persica (Peach)	Rosids	pp.pep	pp.bed	Version 1.0	28689	International Peach Genome et al., 2013
14	Prunus mume (Mei)	Rosids	pm.pep	pm.bed	Version 1.0	31390	Zhang et al., 2012
15	Fragaria vesca (Strawberry)	Rosids	fv.pep	fv.bed	Version 1.1	32831	Shulaev et al., 2011
16	Arabidopsis thaliana (Arabidopsis)	Rosids	at.pep	at.bed	TAIR10	27416	Arabidopsis Genome, 2000
17	Arabidopsis lyrata (Lyrate rockcress)	Rosids	al.pep	al.bed	Version 1.0	32670	Hu et al., 2011
18	Capsella rubella (Capsella)	Rosids	cb.pep	cb.bed	Version 1.0	26521	Slotte et al., 2013
19	Brassica oleracea (Kale)	Rosids	bo.pep	bo.bed	Version 2.1	59225	Liu et al., 2014
20	Brassica rapa (Chinese cabbage)	Rosids	br.pep	br.bed	Version 1.3	40492	Wang et al., 2011
21	Aethionema	Rosids	aeth.pep	aeth.bed	Version 2.5	22230	Haudry et al., 2013
22	Tarenaya	Rosids	tare.pep	tare.bed	Version 5	31580	Cheng et al., 2013
23	Carica papaya (Papaya)	Rosids	cp.pep	cp.bed	ASGPBv0.4	24782	Ming et al., 2008
24	Gossypium raimondii (Cotton)	Rosids	gr.pep	gr.bed	Version 2.1	37505	Paterson et al., 2012
25	Theobroma cacao (Cacao)	Rosids	ta.pep	ta.bed	Version 1.1	29452	Argout et al., 2011
26	Citrus sinensis (Sweet orange)	Rosids	ci.pep	ci.bed	Version 1.1	25379	Xu et al., 2013
27	Eucalyptus grandis (Eucalyptus)	Rosids	eg.pep	eg.bed	Version 1.1	36376	Myburg et al., 2014
28	Vitis vinifera (Grape vine)	Rosids	vv.pep	vv.bed	Genoscope (Aug 2007)	26346	Jaillon et al., 2007
29	Solanum tuberosum (Potato)	Solanace	st.pep	st.bed	Version 3.4	39031	Potato Genome Sequencing et al., 2011
30	Solanum lycopersicum (Tomato)	Solanace	sl.pep	sl.bed	Version 2.4	34727	Tomato Genome, 2012
31	Capsicum annuum (Hot pepper)	Solanace	cu.pep	cu.bed	Version 1.55	34899	Kim et al., 2014
32	Utricularia gibba (Humped bladderwort)	Solanace	ug.pep	ug.bed	CoGe (Jun 2013)	28494	Ibarra-Laclette et al., 2013
33	Actinidia chinensis (Kiwifruit)	Solanace	ah.pep	ah.bed	May_2013	32670	Huang et al., 2013
34	Beta vulgaris (Sugar beet)	Eudicots	bv.pep	bv.bed	RefBeet-1.1	27421	Dohm et al., 2014
35	Nelumbo nucifera (Sacred lotus)	Eudicots	nn.pep	nn.bed	Version 1.0	26685	Ming et al., 2013
36	Triticum urartu (Wheat A-genome)	Monocots	tu.pep	tu.bed	Version 1.0	34879	Ling et al., 2013
37	Hordeum vulgare (Barley)	Monocots	hv.pep	hv.bed	Version 1.0	16598	International Barley Genome Sequencing et al., 2012
38	Brachypodium distachyon (Purple false brome)	Monocots	bd.pep	bd.bed	Version 2.1	31694	International Brachypodium, 2010
39	Oryza sativa (Rice)	Monocots	os.pep	os.bed	Version 7.0	39049	International Rice Genome Sequencing, 2005
40	Zea mays (Maize)	Monocots	zm.pep	zm.bed	Version 6a	63480	Schnable et al., 2009
41	Sorghum bicolor (Sorghum)	Monocots	sb.pep	sb.bed	Version 2.1	33032	Paterson et al., 2009
42	Setaria italica	Monocots	si.pep	si.bed	Version 2.1	35471	Bennetzen et al., 2012
43	Elaeis guineensis (Oil palm)	Monocots	el.pep	el.bed	Version 2.0	30752	Singh et al., 2013
44	Musa acuminata (Banana)	Monocots	ma.pep	ma.bed	July_2012	36542	D'Hont et al., 2012
45	Phalaenopsis equestris (Orchid)	Monocots	pe.pep	pe.bed	Version 5.0	42293	Cai et al., 2015
46	Zostera muelleri (Seagrass)	Monocots	zo.pep	zo.bed	Version 1.0	33245	Golicz et al., 2015
47	Amborella trichopoda (Amborella)	Basal Angiosperm	ar.pep	ar.bed	Version 1.0	26846	Chamala et al., 2013
48	Picea abies (Norway spruce)	Gymnosperm	pa.pep	pa.bed	Version 1.0	66632	Nystedt et al., 2013
49	Selaginella moellendorffii (Selaginella)	Moss	sm.pep	sm.bed	Version 1.0	22273	Banks et al., 2011
50	Physcomitrella patens (Moss)	Moss	ph.pep	ph.bed	Version 3.0	26610	Rensing et al., 2008
51	Chlamydomonas reinhardtii (Green algae)	Green algae	cr.pep	cr.bed	Version 5.5	17741	Merchant et al., 2007

 

 

Citations:

Zhao, T. and Schranz, E., (2019). Network-based microsynteny analysis identifies major differences and genomic outliers in mammalian and angiosperm genomes. Proceedings of the National Academy of Sciences. 116(6), 2165-2174.

Zhao, T., Holmer, R., de Bruijn, S., Angenent, G.C., van den Burg, H.A., and Schranz, M.E. (2017b). Phylogenomic synteny network analysis of MADS-box transcription factor genes reveals lineage-specific transpositions, ancient tandem duplications, and deep positional conservation. The Plant Cell 29, 1278-1292.

Zhao, T., and Schranz, E. (2017a). Network Approaches for Plant Phylogenomic Synteny Analysis. Current Opinion in Plant Biology 36, 129-134.

 

synnet-pipeline's Issues

MCScanX alternative

Hello,

I've never been able to get MCScanX to work on my data, and it unfortunately still isn't working as part of this pipeline. Despite there being plenty of BLAST matches, it never identifies synteny blocks. Is it possible to substitute results from another synteny program like Dagchainer in that step?

Thanks,
Zac

SynetBuilding- Diamond.sh

According to your instruction;
Put all pairs of ".pep" and ".bed" of your genomes under one folder, copy the script (SynetBuilding- Diamond.sh) to the same folder.
after making dir ; mkdir ../DiamondGenomesDB
diamond makedb --in ../$i.pep -d ../DiamondGenomesDB/$i

Error
diamond v0.9.24.125 | by Benjamin Buchfink [email protected]
Licensed under the GNU GPL https://www.gnu.org/licenses/gpl.txt
Check http://github.com/bbuchfink/diamond for updates.
#CPU threads: 4
Scoring parameters: (Matrix=BLOSUM62 Lambda=0.267 K=0.041 Penalties=11/1)
Database file: ../.pep
Opening the database file... No such file or directory
[8.7e-05s]
Error: Error opening file ../.pep**

how to use the results performed by SynetBuild-X.sh?

I performed the shell script SynetBuild-X.sh, finally I got the result looks like:

at0-0b 150.0 at__AT1G48590 at__AT3G17980
at0-1b 150.0 at__AT1G48598 at__AT3G17998
at0-2b 150.0 at__AT1G48600 at__AT3G18000
at1-0b 150.0 at__AT4G25660 at__AT4G25680
at1-1b 150.0 at__AT4G25670 at__AT4G25690

Is the “0-0b” “0-1b” “0-2b” in the first column means the “cluster” in the paper?
And if I set the 1st column as “interaction type”, 2nd column as “source node attribute”, 3rd column as “source node”, 4th column as “target node” in the cytoscape, the result would be right?

tools for phylogenetic profiling

Hi Zhao Tao,
I am interesting about this pipeline. I have read your papers and it's interesting. However, I noticed that you didn't mention the phylogenetic profiling method. Maybe it's simple , but I am new about data analysis. So could you illustrate it ?

Thank you !

Wrong usage of shell scirpts parameter

In line 22 of SynetBuilding-Diamond.sh :

# check whether user had supplied -h or --help . If yes display usage 
	if [[ ( $# == "--help") ||  $# == "-h" ]] 
	then 
		display_usage
		exit 0
	fi 

$# should be $*:

# check whether user had supplied -h or --help . If yes display usage 
	if [[ ( $* == "--help") ||  $* == "-h" ]] 
	then 
		display_usage
		exit 0
	fi 

there might be some species

SynetBuild-X.sh: line 132: detect_collinear_tandem_arrays: command not found

Hi,

Thanks for developing such useful tools,

I am trying to build the synteny network like below but I get this output warning see below.
What might be the reason?
$bash SynetBuild-X.sh 6 3 50 20

..
cCAJNOQ010077972.1.html
dcCAJNOQ010078021.1.html
dcCAJNOQ010078108.1.html
dcCAJNOQ010078245.1.html
Done! [57.500 seconds elapsed]
duplication modes!
Reading BLAST file and pre-processing
Generating BLAST list
94597 matches imported (41496 discarded)
89489 pairwise comparisons
13 alignments generated
Type of dup Code Number
Singleton 0 8187
Dispersed 1 37686
Proximal 2 157
Tandem 3 677
WGD or segmental 4 156
Now we detect collinear tandem arrays.
SynetBuild-X.sh: line 132: detect_collinear_tandem_arrays: command not found
610 alignments generated
Type of dup Code Number
Singleton 0 5090
Dispersed 1 13960
Proximal 2 5247
Tandem 3 2213
WGD or segmental 4 20078
Now we detect collinear tandem arrays.
SynetBuild-X.sh: line 132: detect_collinear_tandem_arrays: command not found

..

Thanks

Minor fix suggestions for infomap.r and Phylogenomic_Profiling.r

Hello, thank you for the SynNet pipeline!

As also follows from the wiki description, and as also commented in the R script, the input file for infomap.r should be tab separated:

SynNet-Pipeline/infomap.r

Line 4 in 1665f03

# The network file should be separated by tab

But the code says different:

SynNet-Pipeline/infomap.r

Line 15 in 1665f03

data <-read.table(args[1],sep=' ',header=F)

The error message is not informative, so it wasn't straightforward to spot this. When I replaced tabs with spaces in my input file it worked.

Also, in Phylogenomic_Profiling.r this way of calling the command variable doesn't seem to work, and again gives an uninformative error; at least for two versions of R that I tried it with in Unix 3.6.1 and 4.0.3 both errored out about the command that contains also this line:

SynNet-Pipeline/Phylogenomic_Profiling.r

Line 72 in 1665f03

filename = args[3].pdf,

As a solution, I uncommented this line:

SynNet-Pipeline/Phylogenomic_Profiling.r

Line 64 in 1665f03

#pdf('args[3].pdf',width=8,height=8,onefile=FALSE)

and changed it into this:

pdf("PhylogenomicProfile.pdf",width=8,height=8,onefile=FALSE)

and commented this line:

SynNet-Pipeline/Phylogenomic_Profiling.r

Line 72 in 1665f03

filename = args[3].pdf,

and uncommented this:

SynNet-Pipeline/Phylogenomic_Profiling.r

Line 75 in 1665f03

#dev.off()

And here is a small suggestion, adding this BinaryDataandTranspose.r into SynNet build.

Species missing from the matrix

Hello,

I started my analysis with 11 species but 1 species
is missing from the phylogeneomics profile file and from the final binary matrix.
What might be the reason for this?

Thanks
Alex

Getting a size error when carrying out Phylogenetic profiling

Hi Dr.Zhao,

I have run into an error when I am carrying out the Phylogenetic profiling of the pipeline. I am using 94 ecdyzosoan genomes and am getting this error:

Loading required package: permute
Loading required package: lattice
This is vegan 2.5-7
Error in hclust(d, method = "ward.D") :
size cannot be NA nor exceed 65536
Execution halted

Could you help me overcome this?

All the best,
Dearbhaile.

My branches are very long

Hi Zhao,

I am writing to ask you on some issue with results. I am using the pipeline with Ecdysozoa genomes of which some have some notoriously long branches. I have attempted many different parameters and increased the number of genomes <50 genomes and I am getting a tree result with just crazily long branches and odd placements. My intuition is that some of the genomes have very few genes as markers and others have more which is causing this skew but I was wondering if you have any advice on how to improve this issue and get cleaner results like you achieved in your angiosperm paper.

Segmentation fault

Hi dude,

I was trying to use your program, but I got this error : SynetBuild-X.sh: line 112: 305923 Segmentation fault (core dumped) /home/asgiraldoc/MCScanX/downstream_analyses/detect_collinear_tandem_arrays -g $i.gff
-b $i.blast -c $i.collinearity -o $i.tandem.collinear

Any idea about what happened here?

Thank you

Error in check_gene_names(seq, annotation) , Sequence names in 'seq' do not match gene names in 'annotation' for:

Hi
I'm trying to run syntenet but I got stacked using check_input(proteomes, annotation); all my files were taken from prokka 1.14.6, so I dont really nkow why theres discrepancies in the names; could you please guide me through the files preparation I didnt get quite well

setwd("~/Documents/syntenet")
library(syntenet)
fasta_dir <- system.file("extdata", "sequences", package = "syntenet")
proteomes <- fasta2AAStringSetlist(fasta_dir) > gff_dir <- system.file("extdata", "annotation", package = "syntenet") > annotation <- gff2GRangesList(gff_dir)
check_input(proteomes, annotation) Error in check_gene_names(seq, annotation) : Sequence names in 'seq' do not match gene names in 'annotation' for:

1. 10432_62_LANL 2. 107V1216_BRAC 3. 1154_74_LANL 4. 11S_UM 5. 1346_SC 6. 1362_SC 7. 146N_ILS 8. 146P_ILS 9. 1587_BI 10. 1627_SC 11. 17609_UM 12. 1Mo_UM 13. 20000_RIPC 14. 2009V_1135_CDC 15. 2010EL_1786_CDCP 16. 2011EL_1271_48x_CDC 17. 2011EL_1271_97x_CDC 18. 2012EL_1094_CDC 19. 2012EL_1096_CDC 20. 2012EL_1097_60x_CDC 21. 2012EL_1097_89x_CDC 22. 2012EL_1759_CDC 23. 2012EL_1760_CDC 24. 2012EL_1765_CDC 25. 2012EL_1776_CDC 26. 2012Env_9_UF 27. 2015V_1118_126x_CDC 28. 2015V_1118_56x_CDC 29. 2018HL24_CCDCP 30. 20390_UM 31. 20478_UM 32. 21B_UM 33. 220075_6_UM 34. 220076_6_UM 35. 22043200_C1_UM 36. 22043202_C1_UM 37. 22043204_C1_UM 38. 22044108_C3_UM 39. 22087102_C2_UM 40. 22087500_C9_UM 41. 2740_80_BI 42. 2740_80_IU 43. 2Mo_UM 44. 31Ki_UM 45. 330033_C1_UM 46. 330073_A_UM 47. 330073_B_UM 48. 330081_UM 49. 330110_UM 50. 330113_UM 51. 330440_C1_UM 52. 330898_C2_UM 53. 330920_A_UM 54. 330920_B_UM 55. 3528_08_CDC 56. 35
   --
   I try to erase those failing files, but when I rerun the script mores sequences show