Salmonella enterica pathogenisity islands (PAI)

PAI Function Insertion Site GenBank Accession 
centisome 63 Iron uptake system, sit operon fhlA AF128999 
CS54 island Intestinal colonization and persistence determinants (shdA, ratA, ratB, sivI, sivH) xseA/yfgK AF140550 
HPI P4-like integrase; located within a high-pathogenicity-island (HPI) region FJ212116 
FJ212115 
Not named putative pathogenicity island protein (ssaD) gene JQ071613 
SESS LEE Type VI secretion system effector tRNA-pheV JQ747523
JQ747540
SGI1 Multi drug resistance (ApCmFlSmSpTc) FJ477835 
Multi drug resistance (ampicillin, gentamicin, streptomycin, spectinomycin, sulfathiazole, tetracycline, and nalidixic acid) thdF/yidY AY463797 
SGI2 Multi drug resistance (trimethoprim, sulfonamides, chloramphenicol, and tetracycline) AY963803 
SPI-1 invA; nonfunctional invasion protein A due to mutation; located on the Salmonella pathogenicity island 1 JN982040 
JN982041 
SPI-1 Type III secretion system, invasion into epithelial cells, apoptosis (InvA, OrgA, SptP, SipA, SipB, SipC, SipD, SopE, prgH) Not published AY956822 
AY956824 
AY956825 
AY956823 
fhlA/mutS AF148689 
NC_006905_P5
NC_003198_P5
NC_004631_P2
SPI-10 Fibrin-like protein (sefD) Not published AY956839 
Phage 46 and the sefA-R chaperone-usher fimbrial operon tRNA-leu NC_003198_P10
SPI-11 sopB, mppA, icdA, envF, msgA, envE, pagD, pagC Gifsy-1 prophage NC_006905_P2
SPI-12 msgA, narP tRNA-pro NC_006905_P4
SPI-13 LysR family transcriptional regulation (gtrB) tRNA-pheV AY956834 
Acetyl-coA dehydrolase (gacD) AY956832 
Trancriptional regulation (gtrA) AY956833 
SPI-14 Electron transfer favoprotein beta subunit (gpiA) Not published AY956835 
Trancriptional regulation (gpiB) AY956836 
SPI-2 spiC; pathogenicity island 2 secreted effector protein JN673271 
JN673273 
Type III secretion system, required for systemic infection and intracellular pathogenesis by facilitating replication of intracellular bacteria within membrane-bound Salmonella-containing vacuoles (SsaJ, SseABC, SpiC) tRNA-valV AJ224892 
spiC; pathogenicity island 2 secreted effector protein JN673268 
JN673270 
Type III secretion system, required for systemic infection and intracellular pathogenesis by facilitating replication of intracellular bacteria within membrane-bound Salmonella-containing vacuoles tRNA-valV AY956826 
AY956827 
AY956828 
AY956830 
AY956829 
AF020808 
spiC; pathogenicity island 2 secreted effector protein JN673269 
JN673272 
Type III secretion system, required for systemic infection and intracellular pathogenesis by facilitating replication of intracellular bacteria within membrane-bound Salmonella-containing vacuoles tRNA-val NC_006905_P3
NC_003198_P3
NC_004631_P1
SPI-3 Invasion, survival in monocytes, Mg2+ uptake (MgtC, B, MarT, MisL) tRNA selC AF106566
AY144489 
Invasion, survival in monocytes, Mg2+ uptake (sugR, rhuMMgtC, B, MarT, MisL), putative fimbrial-like protein(yadC/K./L/M), probable pilin chaperone(ecpD1/D2) AY144490 
Invasion, survival in monocytes, Mg2+ uptake (MgtC, B, MarT, MisL) NC_006905_P6
tRNA-pro NC_003198_P7
SPI-4 Type I secretion system, putative toxin secretion, apoptosis, required for intracellular survival in macrophages ssb/soxSR AF060869
Type I secretion system, putative toxin secretion, apoptosis, required for intracellular survival in macrophages; large secretion protein(siiE) and Type I secretion system components(siiC,D,F) Not published AJ576316
Type I secretion system, putative toxin secretion, apoptosis, required for intracellular survival in macrophages, genes weakly similar to RTX-like toxins ssb/soxSR NC_006905_P7
ssb NC_003198_P8
SPI-5 Invasion, survival in monocytes, Mg2+ uptake (rhuM, MgtC, B, MarT, MisL) tRNA-selC AY144491 
Effector proteins for SPI-1 and SPI-2 (SopB, SigD, PipB) tRNA-serT AF060858 
Invasion, survival in monocytes, Mg2+ uptake (MgtC, B, MarT, MisL) tRNA-selC AY144492 
sopB Not published AF323078 
AF323077 
Effector proteins for SPI-1 and SPI-2 (SopB, SigD, PipB) tRNA-ser NC_006905_P1
NC_003198_P2
SPI-6 Altered metabolic requirements, 4-hydroxybutyrate coA transferase (cat2) tRNA-pheV AF376036 
tRNA-asp NC_003198_P1
SPI-7 Vi exopolysaccharide, SopE prophage and a type IVB pilus operon tRNA-phe NC_003198_P9
NC_004631_P3
tRNA-pheU AF000001 
SPI-8 Two bacteriocin pseudogenes, genes conferring immunity to the bacteriocins tRNA-phe NC_003198_P6
SPI-9 Type I secretory apparatus, large RTX-like protein NC_003198_P4

Adapted from: 

PAIDB v2.0

http://www.paidb.re.kr/browse_pais.php?m=p#Salmonella%20enterica

Depth Coverage Calculations for raw NGS data

Image result for coverage genome

There are two type of coverage:

  • Breadth coverage ( this is related with N50 and contig numbers)   
    • FASTA files run with QUAST to get the metrics
  • Depth of coverage 
    • With a reference genome ( works with alligned files;  bam or sam files)
    • Without a reference genome ( works with FASTQ and FASTA files explained below)

DEPTH COVERAGE WITHOUT REFERENCE GENOME

To calculate the depth coverage of a genome straight from FASTQ and FASTA files without a reference genome we need to have:

  1. Average sequence length
  2. Number of reads
  3. Genome size

Average sequence length

run Fast-QC analysis and Multi-QC analysis (http://multiqc.info/)  to get the average size.

module load FastQC/0.11.6-Java-1.8.0 
fastqc -t 2 -o fastqc path/test_R1.fastq.gz path/test_R2.fastq.gz

After run is complete, transfer the zip files from remote computer to a file in desktop .
This is where I located mine /user/gizemlevent/desktop/fastq/analysis
Multi-QC (should run on computer terminal or Anaconda, I used anaconda environment with manual installment)

git clone https://github.com/ewels/MultiQC.git 
cd MultiQC 
python setup.py install
multiqc /user/gizemlevent/desktop/fastq/analysis -o /user/gizemlevent/desktop/fastq

After this run is complete, go to the location and open the multiqc_fastqc.txt and copy it on excel. Now you have the average read size from both R1 and R2. Take the average of two. This will give you your average sequence lenght.

Number of reads

For those who use Illumina Miseq and who has an account of BaseSpace  can access total number of reads for each samples in the run report. Take that number.

Alternatively, you can run echo $(zcat your.fastq.gz | wc -l) / 4 | bc  to obtain number of reads for each FASTQ files. 

Genome Size 

Run Quast analysis with fasta files on http://quast.bioinf.spbau.ru/  or use command line 

module load QUAST/5.0.2-intel-2018b-Python-3.6.6

python /sw/eb/software/QUAST/5.0.2-intel-2018b-Python-3.6.6/bin/quast.py *.fasta -o quast

Report generated by Quast will provide the total length of genome. Take that number.

Now move to excel to perform the math below:

Formula for depth coverage = ( average sequence length* number of reads)/ genome size 

Below is an example of Escherichia coli draft genome:

total length av.read lengh number of reads coverage depth
5288173 284.9929871 482,180 25.98589693
5292551 288.0475368 516,596 28.11578109

Quotes

“Absence of evidence is not evidence of absence” Carl Sagan

” We do not see the world as it is but as we are. ” Talmud 

“Instead of resisting to changes, surrender. Let life be with you, not against you. If you think ‘My life will be upside down’ don’t worry. How do you know down is not better than upside?” Shams Tabrizi

Antibiotic Resistance Genes

#Aminoglycoside resistance
aac
aac(2′)-Ia
aac(2′)-Ib
aac(2′)-Ic
aac(2′)-Id
aac(2′)-Ie
aac(3)-I
aac(3)-IIIa
aac(3)-IIIb
aac(3)-IIIc
aac(3)-IIa
aac(3)-IIb
aac(3)-IIc
aac(3)-IId
aac(3)-IIe
aac(3)-IVa
aac(3)-IXa
aac(3)-Ia
aac(3)-Ib
aac(3)-Ib-aac(6′)-Ib
aac(3)-Ic
aac(3)-Id
aac(3)-If
aac(3)-Ig
aac(3)-Ih
aac(3)-Ii
aac(3)-Ij
aac(3)-Ik
aac(3)-VIIIa
aac(3)-VIIa
aac(3)-VIa
aac(3)-Va
aac(3)-Vb
aac(3)-Xa
aac(6′)
aac(6′)-31
aac(6′)-32
aac(6′)-33
aac(6′)-I30
aac(6′)-III
aac(6′)-IIa
aac(6′)-IIb
aac(6′)-IIc
aac(6′)-IId
aac(6′)-Ia
aac(6′)-Iaa
aac(6′)-Iad
aac(6′)-Iae
aac(6′)-Iaf
aac(6′)-Iai
aac(6′)-Ib
aac(6′)-Ic
aac(6′)-Id
aac(6′)-Ie
aac(6′)-If
aac(6′)-Ig
aac(6′)-Ih
aac(6′)-Ij
aac(6′)-Ik
aac(6′)-Il
aac(6′)-Im
aac(6′)-In
aac(6′)-Iq
aac(6′)-Ir
aac(6′)-Is
aac(6′)-It
aac(6′)-Iu
aac(6′)-Iv
aac(6′)-Iw
aac(6′)-Iz
aac(6′)-aph(2”)
aac(6′)-Isa
aacA29
aacA43
aadA1
aadA10
aadA11
aadA12
aadA13
aadA14
aadA15
aadA16
aadA17
aadA1b
aadA2
aadA21
aadA22
aadA23
aadA24
aadA3
aadA4
aadA5
aadA6
aadA7
aadA8
aadA8b
aadA9
aadB
aadC
aadD
aadE
aadK
ant(3”)-Ih-aac(6′)-IId
ant(4′)-IIa
ant(4′)-IIb
ant(4′)-Ib
ant(6)-Ia
ant(6)-Ib
ant(9)-Ib
aph(2”)-Ia
aph(2”)-Ib
aph(2”)-Ic
aph(2”)-Id
aph(2”)-Ie
aph(2”)-If
aph(2”)-Ih
aph(3”)-Ia
aph(3”)-Ic
aph(3′)-III
aph(3′)-IIa
aph(3′)-IIb
aph(3′)-IIc
aph(3′)-IV
aph(3′)-Ia
aph(3′)-Ib
aph(3′)-Ic
aph(3′)-Id
aph(3′)-VIII
aph(3′)-VIIa
aph(3′)-VIa
aph(3′)-VIb
aph(3′)-Va
aph(3′)-Vb
aph(3′)-Vc
aph(3′)-XV
aph(4)-Ia
aph(4)-Ib
aph(6)-Ia
aph(6)-Ib
aph(6)-Ic
aph(9)-Ia
aph(9)-Ib
apmA
armA
npmA
rmtA
rmtB
rmtB2
rmtC
rmtD
rmtD2
rmtE
rmtF
rmtG
rmtH
spc
sph
str
strA
strB
aacA-aphD

#Macrolide resistance
car(A)
cfr
optrA
ere(A)
ere(B)
erm(30)
erm(31)
erm(32)
erm(33)
erm(34)
erm(35)
erm(36)
erm(38)
erm(39)
erm(40)
erm(41)
erm(42)
erm(A)
erm(B)
erm(C)
erm(D)
erm(E)
erm(F)
erm(G)
erm(H)
erm(N)
erm(O)
erm(Q)
erm(R)
erm(S)
erm(T)
erm(V)
erm(X)
erm(Y)
erm(Z)
lmr(A)
lmr(B)
lnu(A)
lnu(B)
lnu(C)
lnu(D)
lnu(F)
lsa(B)
lsa(C)
mef(A)
mef(B)
mph(A)
mph(B)
mph(C)
mph(D)
mph(E)
msr(A)
msr(C)
msr(D)
msr(E)
ole(B)
ole(C)
srm(B)
tlr(C)

#Streptogramin B resistance
vat(A)
vat(B)
vat(C)
vat(D)
vat(E)
vat(F)
vga(A)
vga(B)
vgb(A)
vgb(B)
erm(W)
lsa(A)
vga(C)
vga(E)
vga(A)LC

#Phenicol resistance
catB
catB1
catB2
catB3
catB4
catB7
catB8
catB9
catB10
catA3
cat
cat86
cat(pC221)
cat(pC233)
cat(pC194)
catS
catP
cmlA1
cml
cmlV
floR
fexA
pexA
catA1
catA2
cat
catQ
cmrA
cmx

#Quinolone resistance (Mechanism of Action: target modification)
QnrA1
QnrA2
QnrA3
QnrA4
QnrA5
QnrA6
QnrA7
QnrB1
QnrAS
QnrB10
QnrB11
QnrB12
QnrB13
QnrB14
QnrB15
QnrB17
QnrB18
QnrB19
QnrB2
QnrB20
QnrB21
QnrB22
QnrB23
QnrB24
QnrB25
QnrB26
QnrB27
QnrB28
QnrB29
QnrB3
QnrB30
QnrB31
QnrB32
QnrB33
QnrB34
QnrB35
QnrB36
QnrB37
QnrB38
QnrB4
QnrB40
QnrB41
QnrB5
QnrB6
QnrB7
QnrB8
QnrB9
QnrC
QnrD
QnrS1
QnrS2
QnrS3
QnrS4
QnrS5
QnrB16
QnrB42
QnrB43
QnrB44
QnrB45
QnrB46
QnrB47
QnrB48
QnrB49
QnrB50
QnrB51
QnrB52
QnrB53
QnrB54
QnrB56
QnrB57
QnrB58
QnrB59
QnrB60
QnrB61
QnrB62
QnrB64
QnrB65
QnrB66
QnrB67
QnrB68
QnrB69
QnrB70
QnrB71
QnrB72
QnrS6
QnrVC1
QnrVC3
QnrVC4
QnrVC5
QnrVC7
qnrE
qepA
qepA2
aac(6′)Ib-cr
norA
oqxA
oqxB

#sulphonamide resistance
sul1
sul2
sul3

#Tetracycline resistance (Mechanism of action: increased efflux, target protection)
ort(B)
otr(A)
otr(C)
tcr
tet(30)
tet(31)
tet(32)
tet(33)
tet(34)
tet(35)
tet(36)
tet(37)
tet(39)
tet(40)
tet(41)
tet(42)
tet(43)
tet(44)
tet(A)
tet(B)
tet(C)
tet(D)
tet(D)-like
tet(E)
tet(G)
tet(H)
tet(J)
tet(K)
tet(L)
tet(M)
tet(O)
tet(Q)
tet(S)
tet(T)
tet(U)
tet(V)
tet(W)
tet(X)
tet(Y)
tet(Z)
tetA(P)
tetB(P)

#Trimethoprim resistance
dfrA1
dfrA10
dfrA12
dfrA13
dfrA14
dfrA15
dfrA15b
dfrA16
dfrA17
dfrA18
dfrA20
dfrA21
dfrA22
dfrA23
dfrA24
dfrA25
dfrA26
dfrA27
dfrA28
dfrA29
dfrA3
dfrA30
dfrA31
dfrA32
dfrA33
dfrA3b
dfrA5
dfrA6
dfrA7
dfrA8
dfrA9
dfrA-like
dfrB1
dfrB2
dfrB3
dfrB4
dfrB5
dfrB6
dfrB7
dfrB8
dfrC
dfrD
dfrG
dfrK

#Vancomycin
ddlA2-Sc
VanA
VanH-A
VanR-A
VanY-A
VanZ-A
VanS-A
VanX-A
VanB
VanH-B
VanR-B
VanX-B
VanY-B
VanS-B
VanW-B
VanC
VanR-C
VanXY-C
VanS-C
VanT-C
VanD
VanH-D
VanR-D
VanX-D
VanY-D
VanS-D
VanH-E
VanX-E
VanE
VanR-F
VanY-F
VanZ-F
VanS-F
VanR-G
VanXY-G
VanY-G
VanG
VanS-G
VanT-G
VanU-G
VanW-G
VanR-L
VanXY-L
VanL
VanS-L
VanTm-L
VanTr-L
VanH-M
VanR-M
VanX-M
VanY-M
VanM
VanS-M
VanN
VanR-N
VanXY-N
VanS-N
VanT-N
VanX-Sc
VanSc
VanH-Sc
VanPt
VanH-Pt
VanX-Pt
VanY-Pt
VanZ-Pt
VanR-Pt
VanS-Pt
VanW-Pt
VanPt2
VanH-Pt2
VanX-Pt2
VanY-Pt2
VanR-Pt2
VanS-Pt2
VanHE-Pp
VanXE-Pp
VanYF-Pp
VanZF-Pp
VanAE-Pp
VanH-Ac1
VanX-Ac1
VanH-Ac2
VanX-Ac2
VanH-Ao1
VanX-Ao1
VanAo2
VanH-Ao2
VanX-Ao2
VanH-A(Bc)

#Beta-Lactamase
blaCMH-3
blaSGM-7
blaOXA-307
blaOXA-287
blaOXA-293
blaCARB-48
blaL
blaCARB-47
blaVHW-1
blaVHH-1
blaTER-2
blaTER-1
blaSST-1
blaSRT-1
blaSRT-2
blaSPU-1
blaSPG-1
blaSGM-6
blaSGM-5
blaSGM-4
blaSGM-2
blaSGM-1
blaSCO-1
blaPLA-6A
blaPLA-3A
blaPLA-4A
blaPLA-5A
blaPLA2a
blaPLA1a
blaPEDO-3
blaPEDO-2
blaPEDO-1
blaOXA-518
blaOXA-504
blaOXA-493
blaOXA-491
blaOXA-466
blaOXA-465
blaOXA-464
blaOXA-449
blaOXA-448
blaOXA-447
blaOXA-446
blaOXA-427
blaOXA-185
blaOXA-184
blaOXA-159
blaOXA-158
blaOXA-157
blaOXA-156
blaOXA-155
blaOXA-154
blaOXA-153
blaIMI-5
blaVCC-1
blaPAM-1
blaTMB-1
blaTMB-2
blaSMB-1
POM-1
mecA
mecC
blaACC-1
blaACC-2
blaACC-3
blaACC-4
blaCMY-1
blaCMY-10
blaCMY-11
blaCMY-12
blaCMY-13
blaCMY-14
blaCMY-15
blaCMY-16
blaCMY-17
blaCMY-18
blaCMY-19
blaCMY-2
blaCMY-20
blaCMY-21
blaCMY-22
blaCMY-23
blaCMY-24
blaCMY-25
blaCMY-26
blaCMY-27
blaCMY-28
blaCMY-29
blaCMY-3
blaCMY-30
blaCMY-31
blaCMY-32
blaCMY-33
blaCMY-34
blaCMY-35
blaCMY-36
blaCMY-37
blaCMY-38
blaCMY-39
blaCMY-4
blaCMY-40
blaCMY-41
blaCMY-42
blaCMY-43
blaCMY-44
blaCMY-45
blaCMY-46
blaCMY-47
blaCMY-48
blaCMY-49
blaCMY-5
blaCMY-50
blaCMY-51
blaCMY-52
blaCMY-53
blaCMY-54
blaCMY-55
blaCMY-56
blaCMY-57
blaCMY-58
blaCMY-59
blaCMY-6
blaCMY-60
blaCMY-61
blaCMY-62
blaCMY-63
blaCMY-64
blaCMY-65
blaCMY-66
blaCMY-67
blaCMY-68
blaCMY-69
blaCMY-7
blaCMY-70
blaCMY-71
blaCMY-72
blaCMY-73
blaCMY-74
blaCMY-75
blaCMY-76
blaCMY-77
blaCMY-78
blaCMY-79
blaCMY-8
blaCMY-81
blaCMY-9
blaCMY-74
blaCMY-75
blaCMY-76
blaCMY-77
blaCMY-78
blaCMY-79
blaCMY-80
blaCMY-81
blaCMY-82
blaCMY-83
blaCMY-84
blaCMY-85
blaCMY-86
blaCMY-87
blaCMY-88
blaCMY-89
blaCMY-90
blaCMY-91
blaCMY-92
blaCMY-93
blaCMY-94
blaCMY-95
blaCMY-96
blaCMY-97
blaCMY-98
blaCMY-99
blaCMY-100
blaCMY-104
blaCMY-110
blaCTX-M-1
blaCTX-M-10
blaCTX-M-100
blaCTX-M-101
blaCTX-M-102
blaCTX-M-103
blaCTX-M-104
blaCTX-M-105
blaCTX-M-106
blaCTX-M-107
blaCTX-M-108
blaCTX-M-109
blaCTX-M-110
blaCTX-M-11
blaCTX-M-111
blaCTX-M-112
blaCTX-M-113
blaCTX-M-114
blaCTX-M-115
blaCTX-M-116
blaCTX-M-117
blaCTX-M-118
blaCTX-M-119
blaCTX-M-120
blaCTX-M-121
blaCTX-M-122
blaCTX-M-123
blaCTX-M-124
blaCTX-M-125
blaCTX-M-126
blaCTX-M-127
blaCTX-M-128
blaCTX-M-129
blaCTX-M-130
blaCTX-M-131
blaCTX-M-132
blaCTX-M-133
blaCTX-M-134
blaCTX-M-135
blaCTX-M-136
blaCTX-M-137
blaCTX-M-138
blaCTX-M-139
blaCTX-M-140
blaCTX-M-141
blaCTX-M-142
blaCTX-M-143
blaCTX-M-144
blaCTX-M-145
blaCTX-M-146
blaCTX-M-147
blaCTX-M-148
blaCTX-M-149
blaCTX-M-12
blaCTX-M-13
blaCTX-M-14
blaCTX-M-14b
blaCTX-M-15
blaCTX-M-16
blaCTX-M-17
blaCTX-M-18
blaCTX-M-19
blaCTX-M-2
blaCTX-M-20
blaCTX-M-21
blaCTX-M-22
blaCTX-M-23
blaCTX-M-24
blaCTX-M-25
blaCTX-M-26
blaCTX-M-27
blaCTX-M-28
blaCTX-M-29
blaCTX-M-3
blaCTX-M-30
blaCTX-M-31
blaCTX-M-32
blaCTX-M-33
blaCTX-M-34
blaCTX-M-35
blaCTX-M-36
blaCTX-M-37
blaCTX-M-38
blaCTX-M-39
blaCTX-M-4
blaCTX-M-40
blaCTX-M-41
blaCTX-M-42
blaCTX-M-43
blaCTX-M-44
blaCTX-M-45
blaCTX-M-46
blaCTX-M-47
blaCTX-M-48
blaCTX-M-49
blaCTX-M-5
blaCTX-M-50
blaCTX-M-51
blaCTX-M-52
blaCTX-M-53
blaCTX-M-54
blaCTX-M-55
blaCTX-M-56
blaCTX-M-57
blaCTX-M-58
blaCTX-M-59
blaCTX-M-6
blaCTX-M-60
blaCTX-M-61
blaCTX-M-62
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blaCEPA-44
blaCEPA-49
blaCEPA-85
cfxA
cfxA2
cfxA3
cfxA4
cfxA5
cfxA6
blaFAR-1
blaFONA-1
blaFONA-2
blaFONA-3
blaFONA-4
blaFONA-5
blaFONA-6
blaL1
blaKHM-1
blaCEPH-A3
blaA
blaBEL-1
blaBEL-2
blaBEL-3
blaSIM-1
blaGIM-1
blaSPM-1
blaGOB-16
blaGOB-1
blaGOB-16
blaGOB-13
blaGOB-12
blaGOB-10
blaGOB-8
blaGOB-17
blaGOB-15
blaGOB-13
blaGOB-11
blaGOB-9
blaGOB-3
blaGOB-1
blaGOB-5
blaGOB-14
blaGOB-2
blaGOB-4
blaGOB-7
blaGOB-6
blaGOB-18
blaB-14
blaB-12
blaB-10
blaB-13
blaB-11
blaB-9
blaB-3
blaB-2
blaB-8
blaB-5
blaB-7
blaB-6
cfiA14
cfiA13
cfiA16
cfiA15
cfiA12
cfiA11
cfiA9
cfiA7
cfiA5
cfiA3
cfiA2
cfiA8
cfiA6
cfiA4
cfiA10
cfiA1
cphA7
cphA5
cphA2
cphA6
cphA4
cphA8
cphA1
blaPAO
ampH
ampS
blaACI-1
blaAER-1
blaAST-1
blaBES-1
blaBIC-1
blaBIL-1
blaCGB-1
blaCKO-1
blaCME-1
blaCMG
blaDES-1
blaDIM-1
blaEBR-1
blaERP-1
blaJOHN-1
blaLUT-1
blaMAL-1
blaMOR
blaMOR-2
blaMUS-1
blaPME-1
blaRAHN-1
blaRAHN-2
blaSED1
blaSFC-1
blaSFO-1
blaTRU
blaTUS-1
blaZEG-1
blaFRI-1
hugA
imiH
imiS
penA
blaIMP-31
blaBRO-1
blaBRO-2
blaBKC-1

#Rifampicin resistance 
ARR-1
ARR-2
ARR-3
ARR-4
ARR-5
ARR-6
ARR-7

#Fosfomycin resistance (Mech of resistance: Enzymatic inactivation )
fomA
fomB
fos
fosA
fosA2
fosA3
fosA4
fosA5
fosA6
fosA7
fosB
fosB1
fosB2
fosB3
fosB4
fosB5
fosB6
fosC
fosC2
fosD
fosE
fosF
fosG
fosK
fosX

#Fusidic acid resistance
fusA
fusB
fusC
fusD
far1

#Nitroimidazole resistance
nimA
nimB
nimC
nimD
nimE
nimF
nimH
nimI
nimJ

#Colistin resistance ( Mechanism of action:  Target modification)

mcr-1
mcr-2
mcr-1.2
mcr-1.3
mcr-1.4
mcr-1.5
mcr-1.6
mcr-1.7
mcr-1.8
mcr-3
mcr-1.9
mcr-4
mcr-5

NGS Bacterial Genome Bioinformatics tools

Congrats! you have successfully run your sequencer and got your brand new, fresh FASTQ files.. now what?

Here are the steps:

  • Trim the FASTQ file (tool: Trimommatic)
module load Trimmomatic/0.36-Java-1.8.0_92
java -jar $EBROOTTRIMMOMATIC/trimmomatic-0.36.jar PE -phred33 -basein path/test.fastq.gz -baseout path/trimmed/trimmed_test.fastq.gz ILLUMINACLIP:NexteraPE-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36
  • Serotype (tool: SeqSero for Salmonella spp.)
module load SeqSero/f3bd721-intel-2015B-Python-2.7.10
SeqSero.py -m 2 -b sam -i trimmed_1P.fastq.gz  trimmed_2P.fastq.gz &
  • Assembly (tool: Spades)
module load SPAdes/3.11.1-GCCcore-6.3.0
spades.py --careful --memory 5 --threads 2 -1 path/reverse.fastq.gz -2 path/forward.fastq.gz -o test_spades
  • ResFinder (SRST2)
module load SRST2/0.2.0-intel-2015B-Python-2.7.10
srst2 --output resfinder_test --input_pe reverse.fastq.gz --log --gene_db ResFinder.fasta  &
Link to ResFinder.fasta ---> https://github.com/katholt/srst2/tree/master/data
  • PlasmidFinder (SRST2)
module load SRST2/0.2.0-intel-2015B-Python-2.7.10
srst2 --output plasmidfinder_test --input_pe reverse.fastq.gz --log --gene_db PlasmidFinder.fasta  &
Link to get PlasmidFinder.fasta ---> https://github.com/katholt/srst2/tree/master/data
  • MLST (SRST2)

Salmonella enterica:

module load SRST2/0.2.0-intel-2015B-Python-2.7.10
getmlst.py --species "Salmonella enterica" 
srst2 --output test_mlst --input_pe test.fastq.gz --mlst_db Salmonella_enterica.fasta --mlst_definitions senterica.txt --mlst_delimiter ‘_'
Escherichia coli :
getmlst.py --species "Escherichia coli#1"
srst2 --output test_mlst --input_pe test.fastq.gz --mlst_db Escherichia_coli#1.fasta --mlst_definitions ecoli.txt --mlst_delimiter '_'
  • Annotate (tool: PATRIC or RAST)
  • Run SNP analysis (tool: Parsnp and Gingr)
module load Parsnp/1.2-Linux64 
parsnp -d /path_to_the_file -c -r /path_to_the_reference/reference.fasta -o /path_to_the_output
  • Create phylogenetic trees (tool: FigTree)

P.S. I will share the command-line information or/and  links for the job scripts and links soon. 

Serotypes – antigenic formulas

Ref: This table below was created by using WHO Collaborating Centre for Reference and Research on Salmonella Patrick A.D. Grimont, François-Xavier Weill Institut Pasteur, 28 rue du Dr. Roux, 75724 Paris Cedex 15, France

https://www.pasteur.fr/sites/default/files/veng_0.pdf

Type  somatic (O) antigen  Flagellar (H) antigen                   
    H1 (fliC) H2 (fliB) other MLST aroC dnaN hemD hisD purE sucA thrA
Montevideo {6,7,14}{54} g,m,s  –   138 11 41 55 42 34 58 4
Mbandaka 6,7,14 z10 e,n,z15 [z37],[z45 ] 413 15 70 93 78 113 6 68
Reading 1,4,[5],12 e,h 1,5 [R1…] 1628 46 60 10 9 6 12 17
Cerro 6,14,18 z4,z23 [1,5] [z45],[z82] 367 367 14 112 43 123 118 115
Cerro* 18 z4,z23  –   367 367 14 112 43 123 118 115
Rubislaw 11 r e,n,x  – 94 42 46 48 48 40 35 4
Gaminara 16 d 1,7  – 239 50 80 25 85 40 58 4
Anatum 3,{10}{15}{15,34} e,h 1,6 [z64] 64 64 10 14 15 31 25 20
Kentucky 8,20  i z6  – 198 76 14 3 77 64 64 67
Lubbock 6,7 g,m,s n,z15  – 413 15 70 93 78 113 6  
Newport 6,8,20 e,h 1,2  – 118 118 16 2 45 43 36 39
Dublin 1,9,12[Vi]  g,p    –                
Typhimurium 1,4,[5],12 i 1,2   19 10 7 12 9 5 9 2
Typhimurium*  1:4,5,12 i  –                  
Give 3,{10}{15}{15,34} l,v 1,7 [d],[z77 654 111 47 49 42 34 58 4
Agona 1,4,[5],12 f,g,s [1,2]                  
Meleagridis                        
Muenster                        
Eastbourne                        
Cubana                        
Korovi                        

*monophasic types

This table still in progress. .. 

A link to dig in http://www.mgc.ac.cn/cgi-bin/VFs/genus.cgi?Genus=Salmonella

Presentations

Oral Presentations

  1. Probiotic effect of thymol-β-D-glucopyranaside on Escherichia coli in porcine jejunal, cecal and rectal gut compartments- Probiotic Summit, July 06-08 2015, San Francisco, California, USA
  2. Prevalence, quantity and antibiotic resistance profiles of Salmonella enterica during feeding and at slaughter in response to antibiotic use early in the cattle feeding period, Conference of Research Workers in Animal Diseases, December 2016, Chicago, Illinois, USA
  3. Prevalence, quantity and antibiotic resistance profiles of Salmonella enterica during feeding and at slaughter in response to antibiotic use early in the cattle feeding period, Flash talk, January 2017, Graduate Student Association Symposium, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
  4. Prevalence, quantity and antibiotic resistance profiles of Salmonella enterica in response to antibiotic use early in the cattle feeding period, February 2017, Beef Industry Safety Summit, Houston, Texas, USA
  5. Effects of antibiotics on bacterial resistance and serotype diversity of Salmonella enterica originating from lymph nodes, fecal and hide samples of feeder cattle at slaughter November 2018, The 15th International Symposium of Veterinary Epidemiology and Economics, Chiang Mai, Thailand
  6. Exploring evolutionary shifts from Salmonella Mbandaka to Lubbock serotypes in U.S. High Plains feedlot cattle, November 2019, Conference of Research Workers in Animal Diseases, Chicago, Illinois, USA (upcoming presentation).

Poster Presentations

  1. Anti- Salmonella effect of thymol and thymol-β-D-glucopyranasideAmerican Society for Microbiology, 2014 Fall Meeting, Houston, Texas, USA
  2. Anti- Salmonella effect of thymol-β-D-glucopyranaside in porcine jejunal, caecal and rectal gut contents, Congress on Gastrointestinal Function, April, 2015, Chicago, Illinois, USA
  3. Prevalence, quantity and antibiotic resistance profiles of Salmonella enterica during feeding and at slaughter in response to antibiotic use early in the cattle feeding period, 3 min flash talk, January 2017, Graduate Student Assocaition Symposium, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
  4. Prevalence, quantity and antibiotic resistance profiles of Salmonella enterica in response to antibiotic use early in the cattle feeding period,E-poster, February 2017, Beef Industry Safety Summit, Houston, Texas, USA
  5. Long term effects of antimicrobial use in feedlot cattle early in the feeding period on Salmonella enterica spp. population, distribution and antimicrobial resistance profiles in feces during feeding period and on hide and in lymph nodes at slaughter, June 2017, 7th Symposium on Antimicrobial Resistance in Animals and the Environment, Braunschweig, Germany
  6. Use of Whole Genome Sequencing to explore diversity of Salmonella enterica spp. originating from bovine lymph nodes, fecal, hide and environmental feedlot samples, October 2017,  2nd ASM Conference on Rapid Applied Next- Generation Sequencing and Bioinformatic Pipelines, Washington D.C., USA
  7. Use of Whole Genome Sequencing to explore diversity of Salmonella enterica spp. originating from bovine lymph nodes, fecal, hide and environmental feedlot samples, October 2017,  Texas ASM Branch Fall Meting 2018, College Station, Texas, USA

Command-line basics

 

 

 

 

LINUX Command line basics for Bioinformatics Analysis 

  •  $pwd  (Print working directory ) – shows you which directory you are in.
  • $ls or $ll  (Lists) – lists all the files and directories in working directory
    • $ls -l 
    • ls *.fastq – list all fastq files 
  • $history -prints the last 1000 commands in the history
    • $history> history.txt
  • $cd (change directory) – changes the working directory
    • $cd <location> – takes you into directory located
    • $cd .. – takes you one step back directory
      • $cd .. .. ..  – takes you three steps back directory
  • $mkdir -makes new directory in the current directory
    • $mkdir new_dir
  • $touch– creates new file inside of working directory
    • $touch new_file
  • $cp– copies files
    • $cp -r – copies directory
    • $ cp *.fastq – copy all fastq files 
  • *– wild card, selects everything in the specified format
    • *.gb – selects all the GenBank files
  • > -redirects
  • >> -apprends
  • | -pipes
  • $ mv – renames the file or directory name
    • $ mv old_name new_name 
  • $ rm – removes files
    • $ rm -r – removes directory
  • $ rmdir –removes directory
  • -r – option selects for directories
  • -a – option
  • -l – list everything in long format
  • -t– orders file and directories by the time they were last modified
  • -alt – altogether format of -a -l -t
  • -c –  count
  • $ ssh – allows you to log into and work on a remote computer, which can be located anywhere in the world
  • $ scp – (secure copy)- copies the files or directories from a computer to remote computer
    • $ scp my_file.txt gizemlevent@example.com:/scratch/user/transfered_files 
  • $ cut -extract the specified lines
    • $ cut -d “.” -f4 | sort | uniq | wc -l 
    • $ cut -f2,3,4 example.txt > example_subset.txt
    • $ cut -d” ” -f1-6 table.txt
  • $ head  – prints the beginning of the file
    • $head -200 -prints the first 200 lines
  • $ tail  – prints the end of the file
    • $ tail -1000 -prints the last 1000 lines
  • $ cat – prints the whole input of the file
    • $ cat hello.txt  -prints the input of hello.txt
    • $ cat R1.fastq | tail -10000000 > last10mil_R1.fastq
  • $ zcat – unzip the file
  • stdin – standard input
  • stderr -standard error
  • stout – standard output
  • $ sort -sorts the files or directories
    • $ sort hello.csv> sorted_hello.cvs
  • $ grep – searches what you are looking for
    • $ grep “linux” -searched all the linux words in the file
    • $ grep -c hello hello.fasta
      • $grep -c “>” contigs.fasta – counts sequences 
    • $ grep -e hello -e Hello hello.fasta
    • $ grep -v “>” gene.fasta | head -7 > edited_gene.fasta
    • $ grep -v “>” genes.fasta | tail -8 | cat
    • $ grep “>” genes.fasta | sed ‘s/>//’ > gene_names.txt
    • $ grep -A 1 “>geneA” genes.fasta > geneA.fasta
    • $ grep -B 5 “>geneA” genes.fasta > geneA.fasta
  • $ uniq – removes the duplicates only if they are adjacent (uniq doesn’t work without sort)
  • wc -( word count), counts the words
  • $ nano – prints the whole input of the file in nano platform
  • $ sed – finds and replaces the words or characters in the file
    • $ sed s/<old_name>/<new_name>/g  this_file > edited_file 
    • $ sed ‘s/<old_name>/<new_name>/g’ example.fasta > edited_example.fasta
  • $ gunzip – unzips zip files
    • $ gunzip file.gz 
    • $ gunzip -c my_sequence.fasta.gz > new_my_sequence.fastq – unzips and zips back the file to save time
  • $ gzip -zips files
    • $ gzip file.fastq
  • $ module 
    • $ module spider – searches for the module
      • $ module spider bwa 
    • $ module load -loads the module
    • $ module unload -unloads the module
    • $ module avail -shows available modules
  • $ wget -helps to download a file from the web link
  • $ bsub – submits the job
    • $ bsub < <job_file>
  • $ bjobs -lists the running job ids and other information
  • $ bkill – kills the running job with specified ID
    • $ bkill <job_id>
  • $ bhist shows amount of time that job waits in queue, running etc. 
  • $ nohup -runs the process even you log out 
    • $ nohup scp ..
    • $ nohup gunzip ..
  • $ showquota -shows memory available
  • $ dos2unix .fasta or .txt to convert CRLF characters to UNIX format  
  • $ awk ‘BEGIN{sum=0;}{if(NR%4==2){sum+=length($0);}}END{print sum;}’ sequences.fastq.qz – This will count the zipped FASTQ files 
  • $ echo -n “$file,”; awk ‘BEGIN{sum=0;}{if(NR%4==2){sum+=length($0);}}END{print sum;}’  $file; done – to count bases and save in file for FASTQ files 
  • $ echo -n “$file,”; zcat $file | awk ‘BEGIN{sum=0;}{if(NR%4==2){sum+=length($0);}}END{print sum;}’; done -fto count bases and save in file for FASTQ.gz files 

 

Scholarships & Awards

  1. Texas A&M University College of Veterinary Medicine, Graduate Student Association Travel Award, 2019
  2. Aggies Commit Fellowship, Graduate and Professional Student Council & Office of Graduate and Professional Studies, Texas A&M University, 2019
  3. Graduate Student Advanced Development Training Initiative Award, College of Veterinary Medicine and Biomedical sciences, Texas A&M University, 2019
  4. Texas A&M University College of Veterinary Medicine, Graduate Student Association Travel Award, 2018
  5. Research Graduate Research Assistantship, Department of Pathobiology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, TX, USA 2015- 2019
  6. Lechner Scholarship for Spring 2016, Texas A&M University, TX, USA, 2016
  7. Research Scholarship, Agricultural Research Service, USDA, College Station, TX, USA, 2014-2015
  8. Leonardo da Vinci Scholarship, European Union, Helsinki, Finland, 2013
  9. Erasmus Mundus Scholarship, European Union, Barcelona, Spain, 2011
  10. Erasmus Mundus Scholarship, European Union, Bologna, Italy, 2008 – 2009
  11. Erasmus Mundus Scholarship, European Union, Perugia, Italy, 2008