I have two files. One file is a tab separated file containing multiple columns. the other file is a list of gene names. I have to extract only those rows which have the genes listed in file 2 are present in file 1.
I tried the below command but it extract all the rows:
awk 'NR==FNR{a[$0]=1;next} {for(i in a){if($10~i){print;break}}}' File2 file1
File1:
Input line ID Chrom Position Strand Ref. base(s) Alt. base(s) Sample ID HUGO symbol Sequence ontology Protein sequen
3 VAR113_NM-02_TUMOR_DNA chr1 11082255 + G T NM-02_TUMOR_DNA TARDBP MS K263N . PASS het 3 25
4 VAR114_NM-02_TUMOR_DNA chr1 15545868 + G T NM-02_TUMOR_DNA TMEM51 MS V131F . PASS het 3 13
6 VAR116_NM-02_TUMOR_DNA chr1 20676680 + C T NM-02_TUMOR_DNA VWA5B1 SY S970S . PASS het 4 34
7 rs149021429_NM-02_TUMOR_DNA chr1 21554495 + C A NM-02_TUMOR_DNA ECE1 SY S570S . PASS het 3
16 VAR126_NM-02_TUMOR_DNA chr1 39905109 + C T NM-02_TUMOR_DNA MACF1 SY V4069V . PASS het 4 17
21 VAR131_NM-02_TUMOR_DNA chr1 101387378 + G T NM-02_TUMOR_DNA SLC30A7 MS A275S . PASS het 4 45
24 VAR134_NM-02_TUMOR_DNA chr1 113256156 + C A NM-02_TUMOR_DNA PPM1J MS S135I . PASS het 3 9
25 rs201097299_NM-02_TUMOR_DNA chr1 145326106 + A T NM-02_TUMOR_DNA NBPF10 MS M1327L . PASS het 5
26 VAR136_NM-02_TUMOR_DNA chr1 149859281 + T C NM-02_TUMOR_DNA HIST2H2AB SY E62E . PASS het 11
27 VAR137_NM-02_TUMOR_DNA chr1 150529801 + C A NM-02_TUMOR_DNA ADAMTSL4 SY S679S . PASS het 3
28 rs376491237_NM-02_TUMOR_DNA chr1 150532649 + C A NM-02_TUMOR_DNA ADAMTSL4 SY R1068R . PASS het
34 VAR144_NM-02_TUMOR_DNA chr1 160389277 + T A NM-02_TUMOR_DNA VANGL2 SY L226L . PASS het 3 6
35 VAR145_NM-02_TUMOR_DNA chr1 161012389 + C A NM-02_TUMOR_DNA USF1 MS D44Y . PASS het 3 32
37 VAR147_NM-02_TUMOR_DNA chr1 200954042 + G T NM-02_TUMOR_DNA KIF21B MS R1250S . PASS het 3 21
41 rs191896925_NM-02_TUMOR_DNA chr1 207760805 + G T NM-02_TUMOR_DNA CR1 MS G1869W . PASS het 3
42 VAR152_NM-02_TUMOR_DNA chr1 208218427 + C A NM-02_TUMOR_DNA PLXNA2 SY G1208G . PASS het 3 13
43 VAR153_NM-02_TUMOR_DNA chr1 222715425 + A G NM-02_TUMOR_DNA HHIPL2 SY Y349Y . PASS het 10 41
44 VAR154_NM-02_TUMOR_DNA chr1 222715452 + T A NM-02_TUMOR_DNA HHIPL2 SY G340G . PASS het 5 46
45 VAR155_NM-02_TUMOR_DNA chr1 223568296 + G A NM-02_TUMOR_DNA C1orf65 SY G493G . PASS het 3 25
48 VAR158_NM-02_TUMOR_DNA chr2 8931258 + G A NM-02_TUMOR_DNA KIDINS220 MS P458L . PASS het 3 13
51 VAR161_NM-02_TUMOR_DNA chr2 37229656 + C A NM-02_TUMOR_DNA HEATR5B MS G1704C . PASS het 4 9
60 VAR170_NM-02_TUMOR_DNA chr2 84775506 + G T NM-02_TUMOR_DNA DNAH6 MS Q427H . PASS het 3 20
63 VAR173_NM-02_TUMOR_DNA chr2 86378563 + C A NM-02_TUMOR_DNA IMMT MS A420S . PASS het 6 29
64 VAR174_NM-02_TUMOR_DNA chr2 86716546 + G T NM-02_TUMOR_DNA KDM3A MS C1140F . PASS het 3 18
65 VAR175_NM-02_TUMOR_DNA chr2 96852612 + C A NM-02_TUMOR_DNA STARD7 SY L323L . PASS het 2 2
67 VAR177_NM-02_TUMOR_DNA chr2 121747740 + C A NM-02_TUMOR_DNA GLI2 MS P1417H . PASS het 2 2
71 rs199770435_NM-02_TUMOR_DNA chr2 130872871 + C T NM-02_TUMOR_DNA POTEF SY G184G . PASS het 8
72 rs199695856_NM-02_TUMOR_DNA chr2 132919171 + A G NM-02_TUMOR_DNA ANKRD30BL SY H36H . PASS het
73 rs111295191_NM-02_TUMOR_DNA chr2 132919192 + G A NM-02_TUMOR_DNA ANKRD30BL SY N29N . PASS het
76 VAR186_NM-02_TUMOR_DNA chr2 167084231 + T A NM-02_TUMOR_DNA SCN9A SY A1392A . PASS het 3 19
77 VAR187_NM-02_TUMOR_DNA chr2 168100115 + C G NM-02_TUMOR_DNA XIRP2 MS T738S . PASS het 9 49
78 VAR188_NM-02_TUMOR_DNA chr2 179343033 + G T NM-02_TUMOR_DNA FKBP7 MS A65D . PASS het 3 7
79 VAR189_NM-02_TUMOR_DNA chr2 179544108 + G C NM-02_TUMOR_DNA TTN MS P11234A . PASS het 3 17
82 VAR192_NM-02_TUMOR_DNA chr2 220074164 + G T NM-02_TUMOR_DNA ZFAND2B MS E92D . PASS het 2 2
83 VAR193_NM-02_TUMOR_DNA chr2 220420892 + C A NM-02_TUMOR_DNA OBSL1 MS G1487W . PASS het 3 9
84 rs191578275_NM-02_TUMOR_DNA chr2 233273263 + C A NM-02_TUMOR_DNA ALPPL2 MS P279Q . PASS het 3
86 VAR196_NM-02_TUMOR_DNA chr2 241815391 + G T NM-02_TUMOR_DNA AGXT SY L272L . PASS het 3 10
88 VAR198_NM-02_TUMOR_DNA chr3 9484995 + C T NM-02_TUMOR_DNA SETD5 SG R361* . PASS het 3 18
96 VAR206_NM-02_TUMOR_DNA chr3 49848502 + G T NM-02_TUMOR_DNA UBA7 MS P382H . PASS het 5 38
102 VAR212_NM-02_TUMOR_DNA chr3 58302669 + G T NM-02_TUMOR_DNA RPP14 MS L89F . PASS het 3 30
103 VAR213_NM-02_TUMOR_DNA chr3 63981750 + C A NM-02_TUMOR_DNA ATXN7 MS T751K . PASS het 3 13
104 rs146577101_NM-02_TUMOR_DNA chr3 97868656 + C T NM-02_TUMOR_DNA OR5H14 MS R143W . PASS het 4
107 rs58176285_NM-02_TUMOR_DNA chr3 123419183 + G A NM-02_TUMOR_DNA MYLK SY A1044A . PASS het 18
108 VAR218_NM-02_TUMOR_DNA chr3 123419189 + C T NM-02_TUMOR_DNA MYLK SY K1042K . PASS het 23 174
115 VAR225_NM-02_TUMOR_DNA chr3 183753779 + C A NM-02_TUMOR_DNA HTR3D MS P91T . PASS het 4 48
File2:
FBN1
HELZ
RALGPS2
DYNC1I2
NFE2L2
POSTN
INO80
I want those row which contains these genes.
So if I am following you correctly you just want to search $9 in file1 using the genes in file2 and I add MYLK to the list I get:
Maybe:
awk 'NR==FNR{A[$1];next}$9 in A' file2 file1
**empty line** (since `MYLK` was found after the line break it is included
107 rs58176285_NM-02_TUMOR_DNA chr3 123419183 + G A NM-02_TUMOR_DNA MYLK SY A1044A . PASS het 18
108 VAR218_NM-02_TUMOR_DNA chr3 123419189 + C T NM-02_TUMOR_DNA MYLK SY K1042K . PASS het 23 174
To remove the line break from the output:
awk 'NR==FNR{A[$1];next}$9 in A' file2 file1 | awk '!/^$/'
107 rs58176285_NM-02_TUMOR_DNA chr3 123419183 + G A NM-02_TUMOR_DNA MYLK SY A1044A . PASS het 18
108 VAR218_NM-02_TUMOR_DNA chr3 123419189 + C T NM-02_TUMOR_DNA MYLK SY K1042K . PASS het 23 174
Related
I have file 1 with 5778 lines with 15 columns.
Sample from output_armlympho.txt:
NUMBER CHROM POS ID REF ALT A1 TEST OBS_CT BETA SE L95 U95 T_STAT P
42484 1 18052645 rs260514:18052645:G:A G A G ADD 1597 0.0147047 0.0656528 -0.113972 0.143382 0.223977 0.822804
42485 1 18054638 rs35592535:18054638:GC:G GC G G ADD 1597 0.0138673 0.0269643 -0.0389816 0.0667163 0.514286 0.607124
42486 7 18054785 rs1572792:18054785:G:A G A A ADD 1597 -0.0126002 0.0256229 -0.0628202
I have another file with 25958247 lines and 16 columns
Sample from file1:
column1 column2 column3 column4 column5 column6 column7 column8 column9 column10 column11 column12 column13 column14 column15 column16
1 chr1_10000044_A_T_b38 ENS 171773 29 30 0.02 0.33 0.144 0.14 chr1 10000044 A T chr1 10060102
2 chr7_10000044_A_T_b38 ENS -58627 29 30 0.024 0.26 0.16 0.15 chr7 10000044 A T chr7 18054785
4 chr1_10000044_A_T_b38 ENS 89708 29 30 0.0 0.03 -0.0 0.038 chr1 10000044 A T chr1 18054638
5 chr1_10000044_A_T_b38 ENS -472482 29 30 0.02 0.16 0.11 0.07 chr1 10000044 A T chr1 18052645
I want to merge these files together so that the second and third column from file 1 (CHROM POS) exactly matches the 15th and 16th columns from file 2 (column15 column16). However a problem is that in column15, the format is chr[number] e.g. chr1 and in file 1 is just 1. So I need a way to match 1 to chr1 or 7 to chr7 and via position. There may also be repeated lines in file 2. E.g. repeated values that are the same in column15 and column16. Both files aren't ordered in the same way.
Expected output: (outputs all the columns from file 1 and 2).
column1 column2 column3 column4 column5 column6 column7 column8 column9 column10 column11 column12 column13 column14 column15 column16 NUMBER CHROM POS ID REF ALT A1 TEST OBS_CT BETA SE L95 U95 T_STAT P
2 chr7_10000044_A_T_b38 ENS -58627 29 30 0.024 0.26 0.16 0.15 chr7 10000044 A T chr7 18054785 42486 7 18054785 42486 7 18054785 rs1572792:18054785:G:A G A A ADD 1597 -0.0126002 0.0256229 -0.0628202
4 chr1_10000044_A_T_b38 ENS 89708 29 30 0.0 0.03 -0.0 0.038 chr1 10000044 A T chr1 18054638 42485 1 18054638 rs35592535:18054638:GC:G GC G G ADD 1597 0.0138673 0.0269643 -0.0389816 0.0667163 0.514286 0.607124
5 chr1_10000044_A_T_b38 ENS -472482 29 30 0.02 0.16 0.11 0.07 chr1 10000044 A T chr1 18052645 42484 1 18052645 rs260514:18052645:G:A G A G ADD 1597 0.0147047 0.0656528 -0.113972 0.143382 0.223977 0.822804
Current attempt:
awk 'NR==FNR {Tmp[$3] = $16 FS $4; next} ($16 in Tmp) {print $0 FS Tmp[$16]}' output_armlympho.txt file1 > test
Assumptions:
within the file output_armlympho.txt the combination of the 2nd and 3rd columns are unique
One awk idea:
awk '
FNR==1 { if (header) print $0,header; else header=$0; next }
FNR==NR { lines["chr" $2,$3]=$0; next }
($15,$16) in lines { print $0, lines[$15,$16] }
' output_armlympho.txt file1
This generates:
column1 column2 column3 column4 column5 column6 column7 column8 column9 column10 column11 column12 column13 column14 column15 column16 NUMBER CHROM POS ID REF ALT A1 TEST OBS_CT BETA SE L95 U95 T_STAT P
2 chr7_10000044_A_T_b38 ENS -58627 29 30 0.024 0.26 0.16 0.15 chr7 10000044 A T chr7 18054785 42486 7 18054785 rs1572792:18054785:G:A G A A ADD 1597 -0.0126002 0.0256229 -0.0628202
4 chr1_10000044_A_T_b38 ENS 89708 29 30 0.0 0.03 -0.0 0.038 chr1 10000044 A T chr1 18054638 42485 1 18054638 rs35592535:18054638:GC:G GC G G ADD 1597 0.0138673 0.0269643 -0.0389816 0.0667163 0.514286 0.607124
5 chr1_10000044_A_T_b38 ENS -472482 29 30 0.02 0.16 0.11 0.07 chr1 10000044 A T chr1 18052645 42484 1 18052645 rs260514:18052645:G:A G A G ADD 1597 0.0147047 0.0656528 -0.113972 0.143382 0.223977 0.822804
I've got some data like:
val
chr1
chr2
1
a
x1
2
a
y2
3
a
z3
4
b
x1
5
b
y2
6
b
z3
I want to select data, so that in the result if chr1 = 'a' then chr2 only has x1, otherwise don't filter chr2 i.e :
val
chr1
chr2
1
a
x1
4
b
x1
5
b
y2
6
b
z3
I have the restriction due to a platform I'm forced to use, that I can only filter using the WHERE condition of the query, on Redshift.
You may use the following logic:
SELECT *
FROM yourTable
WHERE chr1 <> 'a' OR chr2 = 'x1';
So a whitelisted record is one which does not have chr1 = 'a', or a record which does have this value but also has chr2 = 'x1'.
I am working with sequencing data, but I think the problem applies to different range-value datatypes.
I want to combine several experiments of read counts(values) from a set DNA regions that have a start and end position (ranges), into added up counts for other set of DNA regions, which generally englobe many of the primary regions. Like in the following example:
Giving the following table A with ranges and counts:
feature start end count1 count2 count3
gene1 1 10 100 30 22
gene2 15 40 20 10 6
gene3 50 70 40 11 7
gene4 100 150 23 15 9
and the following table B (with new ranges):
feature start end
range1 1 45
range2 55 160
I would like to get the following count table with the new ranges:
feature start end count1 count2 count3
range1 1 45 120 40 28
range2 55 160 63 26 16
Just to simplify, if there is at least some overlap (at least a fraction a feature in table A is contained in feature in table B), it should be added up. Any idea of a tool available doing that or a script in perl, python or R? I am counting the sequencing reads with bedtools multicov, but as far as I searched there is no other functionality doing what I want. Any idea?
Thanks.
We can do this by:
Creating an artificial key column
Perform an outer join (mxn)
Filter on the start OR end value being between our ranges
pandas.DataFrame.groupby on feature and sum the count columns
Finally concat the output to df2, to get desired output
df1['key'] = 'A'
df2['key'] = 'A'
df3 = pd.merge(df1,df2, on='key', how='outer')
df4 = df3[(df3.start_x.between(df3.start_y, df3.end_y)) | (df3.end_x.between(df3.start_y, df3.end_y))]
df5 = df4.groupby('feature_y').agg({'count1':'sum',
'count2':'sum',
'count3':'sum'}).reset_index()
df_final = pd.concat([df2.drop(['key'], axis=1), df5.drop(['feature_y'], axis=1)], axis=1)
output
print(df_final)
feature start end count1 count2 count3
0 range1 1 45 120 40 28
1 range2 55 160 63 26 16
You can use apply() and pd.concat() with a custom function where a corresponds to your first dataframe and b corresponds to your second dataframe:
def find_englobed(x):
englobed = a[(a['start'].between(x['start'], x['end'])) | (a['end'].between(x['start'], x['end']))]
return englobed[['count1','count2','count3']].sum()
pd.concat([b, b.apply(find_englobed, axis=1)], axis=1)
Yields:
feature start end count1 count2 count3
0 range1 1 45 120 40 28
1 range2 55 160 63 26 16
If it can help somebody, based on #rahlf23 answer, I modified it to make it more general, considering that on one side, the counting columns can be more, and that besides the range, it is also important to be on the right chromosome.
So if table "a" is:
feature Chromosome start end count1 count2 count3
gene1 Chr1 1 10 100 30 22
gene2 Chr1 15 40 20 10 6
gene3 Chr1 50 70 40 11 7
gene4 Chr1 100 150 23 15 9
gene5 Chr2 5 30 24 17 2
gene5 Chr2 40 80 4 28 16
and table "b" is:
feature Chromosome start end
range1 Chr1 1 45
range2 Chr1 55 160
range3 Chr2 10 90
range4 Chr2 100 200
with the following python script:
import pandas as pd
def find_englobed(x):
englobed = a[(a['Chromosome'] == x['Chromosome']) & (a['start'].between(x['start'], x['end']) | (a['end'].between(x['start'], x['end'])))]
return englobed[list(a.columns[4:])].sum()
pd.concat([b, b.apply(find_englobed, axis=1)], axis=1)
Now with a['Chromosome'] == x['Chromosome'] & I ask for them to be in the same Chromosome, and with list(a.columns[4:]) I get all the columns from the 5th until the end, being independent on the number of count columns.
I obtain the following result:
feature Chromosome start end count1 count2 count3
range1 Chr1 1 45 120.0 40.0 28.0
range2 Chr1 55 160 63.0 26.0 16.0
range3 Chr2 10 90 28.0 45.0 18.0
range4 Chr2 100 200 0.0 0.0 0.0
I am not sure why the obtained counts are with floating points.. any comment?
If you are doing genomics in pandas you might want to look into pyranges:
import pyranges as pr
c = """feature Chromosome Start End count1 count2 count3
gene1 Chr1 1 10 100 30 22
gene2 Chr1 15 40 20 10 6
gene3 Chr1 50 70 40 11 7
gene4 Chr1 100 150 23 15 9
gene5 Chr2 5 30 24 17 2
gene5 Chr2 40 80 4 28 16
"""
c2 = """feature Chromosome Start End
range1 Chr1 1 45
range2 Chr1 55 160
range3 Chr2 10 90
range4 Chr2 100 200 """
gr, gr2 = pr.from_string(c), pr.from_string(c2)
j = gr2.join(gr).drop(like="_b")
# +------------+--------------+-----------+-----------+-----------+-----------+-----------+
# | feature | Chromosome | Start | End | count1 | count2 | count3 |
# | (object) | (category) | (int32) | (int32) | (int64) | (int64) | (int64) |
# |------------+--------------+-----------+-----------+-----------+-----------+-----------|
# | range1 | Chr1 | 1 | 45 | 100 | 30 | 22 |
# | range1 | Chr1 | 1 | 45 | 20 | 10 | 6 |
# | range2 | Chr1 | 55 | 160 | 40 | 11 | 7 |
# | range2 | Chr1 | 55 | 160 | 23 | 15 | 9 |
# | range3 | Chr2 | 10 | 90 | 24 | 17 | 2 |
# | range3 | Chr2 | 10 | 90 | 4 | 28 | 16 |
# +------------+--------------+-----------+-----------+-----------+-----------+-----------+
# Unstranded PyRanges object has 6 rows and 7 columns from 2 chromosomes.
# For printing, the PyRanges was sorted on Chromosome.
df = j.df
fs = {"Chromosome": "first", "Start":
"first", "End": "first", "count1": "sum", "count2": "sum", "count3": "sum"}
result = df.groupby("feature".split()).agg(fs)
# Chromosome Start End count1 count2 count3
# feature
# range1 Chr1 1 45 120 40 28
# range2 Chr1 55 160 63 26 16
# range3 Chr2 10 90 28 45 18
I am trying to compare two files and combine different columns of each. The example files are:
1.txt
chr8 12 24 . . + chr1 11 12 XX4 -
chr3 22 33 . . + chr4 60 61 XXX9 -
2.txt
chr1 11 1 X1 X2 11 12 2.443 0.843 +1 SXSD 1.3020000
chr1 11 2 X3 X4 11 12 0.888 0.833 -1 XXSD -28.887787
chr1 11 3 X5 X6 11 12 0.888 0.843 +1 XXSD 2.4909883
chr1 12 4 X7 X8 11 12 0.888 0.813 -1 CMKY 0.0009223
chr1 12 5 X9 X10 11 12 0.888 0.010 -1 XASD 0.0009223
chr1 12 6 X11 X12 11 12 0.888 0.813 -1 XUPS 0.10176998
I want to compare the 1st,6th and 7th columns of 2.txt, with 7th,8th and 9th columns of 1.txt, and if there is a match, I want to print the whole line of 1.txt with 3th and 12th columns of 2.txt.
The expected output is :
chr8 12 24 . . + chr1 11 12 XX4 - 1 1.3020000
chr8 12 24 . . + chr1 11 12 XX4 - 2 -28.887787
chr8 12 24 . . + chr1 11 12 XX4 - 3 2.4909883
chr8 12 24 . . + chr1 11 12 XX4 - 4 0.0009223
chr8 12 24 . . + chr1 11 12 XX4 - 5 0.0009223
chr8 12 24 . . + chr1 11 12 XX4 - 6 0.10176998
My trial is with awk:
awk 'NR==FNR{ a[$1,$6,$7]=$3"\t"$12; next } { s=SUBSEP; k=$7 s $8 s $9 }k in a{ print $0,a[k] }' 2.txt 1.txt
It outputs only the last match and I cannot make it print all matches:
chr8 12 24 . . + chr1 11 12 XX4 - 6 0.10176998
How can I repetitively search and print all matches?
You're making it much harder than it has to be by reading the 2nd file first.
$ cat tst.awk
NR==FNR { a[$7,$8,$9] = $0; next }
($1,$6,$7) in a { print a[$1,$6,$7], $3, $12 }
$ awk -f tst.awk 1.txt 2.txt
chr8 12 24 . . + chr1 11 12 XX4 - 1 1.3020000
chr8 12 24 . . + chr1 11 12 XX4 - 2 -28.887787
chr8 12 24 . . + chr1 11 12 XX4 - 3 2.4909883
chr8 12 24 . . + chr1 11 12 XX4 - 4 0.0009223
chr8 12 24 . . + chr1 11 12 XX4 - 5 0.0009223
chr8 12 24 . . + chr1 11 12 XX4 - 6 0.10176998
Extended AWK solution:
awk 'NR==FNR{ s=SUBSEP; k=$1 s $6 s $7; a[k]=(k in a? a[k]"#":"")$3"\t"$12; next }
{ s=SUBSEP; k=$7 s $8 s $9 }
k in a{ len=split(a[k], b, "#"); for (i=1;i<=len;i++) print $0,b[i] }' 2.txt 1.txt
s=SUBSEP; k=$1 s $6 s $7 - constructing key k value comprised of the 1st, 6th and 7th fields of hte file 2.txt
a[k]=(k in a? a[k]"#":"")$3"\t"$12 - concatenate the $3"\t"$12 sequences with custom separator # within the same group (group presented by k)
s=SUBSEP; k=$7 s $8 s $9 - constructing key k value comprised of the 7th, 8th and 9th fields of the file 1.txt
len=split(a[k], b, "#"); - split previously accumulated sequences into array b by separator #
The output:
chr8 12 24 . . + chr1 11 12 XX4 - 1 1.3020000
chr8 12 24 . . + chr1 11 12 XX4 - 2 -28.887787
chr8 12 24 . . + chr1 11 12 XX4 - 3 2.4909883
chr8 12 24 . . + chr1 11 12 XX4 - 4 0.0009223
chr8 12 24 . . + chr1 11 12 XX4 - 5 0.0009223
chr8 12 24 . . + chr1 11 12 XX4 - 6 0.10176998
Hi I have a big data set and i want to match two files based on $5 from file 1 and $1 or $3 of file 2 and print file 1 which match with file 2. In addition, i want to print $5 and $6 of file 2 in file 1 after matching.
file 1
7 81 1 47 32070
7 83 1 67 29446
7 92 1 84 28234
file 2
32070 0 0 19360101 HF 8 0 M C
28234 0 0 19350101 HF 8 0 M C
124332 0 0 19340101 HF 8 0 M C
29446 0 0 19340101 HF 8 0 M C
I would like to print like this
7 81 1 47 32070 HF 8
7 83 1 67 29446 HF 8
7 92 1 84 28234 HF 8
This awk one-liner should do the job:
awk 'NR==FNR{a[$1]=$5 FS $6;next}$0=$0 FS a[$NF]' f2 f1
If give it a test on your example input files:
kent$ awk 'NR==FNR{a[$1]=$5 FS $6;next}$0=$0 FS a[$NF]' f2 f1
7 81 1 47 32070 HF 8
7 83 1 67 29446 HF 8
7 92 1 84 28234 HF 8