Python Packages: Pandas and BioPython

Pandas

Python has lots of packages that allows you to manipulate data in different ways. For example, Pandas allows you to manipulate dataset with a similar dataframe structure that the language R has.

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive.

Pandas is well suited for many different kinds of data:

Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
Ordered and unordered (not necessarily fixed-frequency) time series data.
Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels
Any other form of observational / statistical data sets. The data need not be labeled at all to be placed into a pandas data structure.

For today we are going to learn basics commands that will allow us to upload data from a CSV, perform commands that will allow us to get general data from the dataset and filter by column.

Importing the Pandas package

To work woth any package in python you have to download it through your package manager (in the case of this workshop would be Conda), and then import it. For this workshop, the conda enviroment already had installed Pandas.

The following code will allow you to import the package Pandas to your

import pandas as pd

How to load a CSV into Pandas

df = pd.read_csv('../python_lesson/combined_tidy_vcf.csv')

Note: The directory path were the dataset is located may be in a diferent place than this example.

Getting information about the dataset

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 801 entries, 0 to 800
Data columns (total 29 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   sample_id      801 non-null    object 
 1   CHROM          801 non-null    object 
 2   POS            801 non-null    int64  
 3   ID             0 non-null      float64
 4   REF            801 non-null    object 
 5   ALT            801 non-null    object 
 6   QUAL           801 non-null    float64
 7   FILTER         0 non-null      float64
 8   INDEL          801 non-null    bool   
 9   IDV            101 non-null    float64
 10  IMF            101 non-null    float64
 11  DP             801 non-null    int64  
 12  VDB            801 non-null    float64
 13  RPB            28 non-null     float64
 14  MQB            28 non-null     float64
 15  BQB            28 non-null     float64
 16  MQSB           753 non-null    float64
 17  SGB            801 non-null    float64
 18  MQ0F           801 non-null    float64
 19  ICB            0 non-null      float64
 20  HOB            0 non-null      float64
 21  AC             801 non-null    int64  
 22  AN             801 non-null    int64  
 23  DP4            801 non-null    object 
 24  MQ             801 non-null    int64  
 25  Indiv          801 non-null    object 
 26  gt_PL          801 non-null    object 
 27  gt_GT          801 non-null    int64  
 28  gt_GT_alleles  801 non-null    object 
dtypes: bool(1), float64(14), int64(6), object(8)
memory usage: 176.1+ KB

Getting the first rows of the dataset

df.head()

	sample_id	CHROM	POS	ID	REF	ALT	QUAL	FILTER	INDEL	IDV	...	ICB	HOB	AC	AN	DP4	MQ	Indiv	gt_PL	gt_GT	gt_GT_alleles
0	SRR2584863	CP000819.1	9972	NaN	T	G	91.0	NaN	False	NaN	...	NaN	NaN	1	1	0,0,0,4	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	121,0	1	G
1	SRR2584863	CP000819.1	263235	NaN	G	T	85.0	NaN	False	NaN	...	NaN	NaN	1	1	0,1,0,5	33	/home/dcuser/dc_workshop/results/bam/SRR258486...	112,0	1	T
2	SRR2584863	CP000819.1	281923	NaN	G	T	217.0	NaN	False	NaN	...	NaN	NaN	1	1	0,0,4,5	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	247,0	1	T
3	SRR2584863	CP000819.1	433359	NaN	CTTTTTTT	CTTTTTTTT	64.0	NaN	True	12.0	...	NaN	NaN	1	1	0,1,3,8	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	91,0	1	CTTTTTTTT
4	SRR2584863	CP000819.1	473901	NaN	CCGC	CCGCGC	228.0	NaN	True	9.0	...	NaN	NaN	1	1	1,0,2,7	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	255,0	1	CCGCGC

5 rows × 29 columns

Getting the last rows of the dataset

df.tail()

	sample_id	CHROM	POS	ID	REF	ALT	QUAL	FILTER	INDEL	IDV	...	ICB	HOB	AC	AN	DP4	MQ	Indiv	gt_PL	gt_GT	gt_GT_alleles
796	SRR2589044	CP000819.1	3481820	NaN	A	G	225.0	NaN	False	NaN	...	NaN	NaN	1	1	0,0,4,8	60	/home/dcuser/dc_workshop/results/bam/SRR258904...	255,0	1	G
797	SRR2589044	CP000819.1	3893550	NaN	AG	AGG	101.0	NaN	True	4.0	...	NaN	NaN	1	1	0,0,3,1	52	/home/dcuser/dc_workshop/results/bam/SRR258904...	131,0	1	AGG
798	SRR2589044	CP000819.1	3901455	NaN	A	AC	70.0	NaN	True	3.0	...	NaN	NaN	1	1	0,0,3,0	60	/home/dcuser/dc_workshop/results/bam/SRR258904...	100,0	1	AC
799	SRR2589044	CP000819.1	4100183	NaN	A	G	177.0	NaN	False	NaN	...	NaN	NaN	1	1	0,0,3,5	60	/home/dcuser/dc_workshop/results/bam/SRR258904...	207,0	1	G
800	SRR2589044	CP000819.1	4431393	NaN	TGG	T	225.0	NaN	True	10.0	...	NaN	NaN	1	1	0,0,4,6	60	/home/dcuser/dc_workshop/results/bam/SRR258904...	255,0	1	T

5 rows × 29 columns

Getting the name of the columns

df.columns

Index(['sample_id', 'CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER',
       'INDEL', 'IDV', 'IMF', 'DP', 'VDB', 'RPB', 'MQB', 'BQB', 'MQSB', 'SGB',
       'MQ0F', 'ICB', 'HOB', 'AC', 'AN', 'DP4', 'MQ', 'Indiv', 'gt_PL',
       'gt_GT', 'gt_GT_alleles'],
      dtype='object')

Identifiying Null values

df.isnull()

	sample_id	CHROM	POS	ID	REF	ALT	QUAL	FILTER	INDEL	IDV	...	ICB	HOB	AC	AN	DP4	MQ	Indiv	gt_PL	gt_GT	gt_GT_alleles
0	False	False	False	True	False	False	False	True	False	True	...	True	True	False	False	False	False	False	False	False	False
1	False	False	False	True	False	False	False	True	False	True	...	True	True	False	False	False	False	False	False	False	False
2	False	False	False	True	False	False	False	True	False	True	...	True	True	False	False	False	False	False	False	False	False
3	False	False	False	True	False	False	False	True	False	False	...	True	True	False	False	False	False	False	False	False	False
4	False	False	False	True	False	False	False	True	False	False	...	True	True	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
796	False	False	False	True	False	False	False	True	False	True	...	True	True	False	False	False	False	False	False	False	False
797	False	False	False	True	False	False	False	True	False	False	...	True	True	False	False	False	False	False	False	False	False
798	False	False	False	True	False	False	False	True	False	False	...	True	True	False	False	False	False	False	False	False	False
799	False	False	False	True	False	False	False	True	False	True	...	True	True	False	False	False	False	False	False	False	False
800	False	False	False	True	False	False	False	True	False	False	...	True	True	False	False	False	False	False	False	False	False

801 rows × 29 columns

Identifiying Null values and counting them

df.isnull().sum()

sample_id          0
CHROM              0
POS                0
ID               801
REF                0
ALT                0
QUAL               0
FILTER           801
INDEL              0
IDV              700
IMF              700
DP                 0
VDB                0
RPB              773
MQB              773
BQB              773
MQSB              48
SGB                0
MQ0F               0
ICB              801
HOB              801
AC                 0
AN                 0
DP4                0
MQ                 0
Indiv              0
gt_PL              0
gt_GT              0
gt_GT_alleles      0
dtype: int64

Filtering a column

In this example we are going to filter the dataset to alter allele sequences of at least length 10. To do this we need to apply a transformation to the ALT column using apply.

df['ALT'].apply(len)

0      1
1      1
2      1
3      9
4      6
      ..
796    1
797    3
798    2
799    1
800    1
Name: ALT, Length: 801, dtype: int64

This vector of lengths can then be compared to a minimum length to negerate a boolean vector.

min_len = 10
df['ALT'].apply(len) >= min_len

0      False
1      False
2      False
3      False
4      False
       ...  
796    False
797    False
798    False
799    False
800    False
Name: ALT, Length: 801, dtype: bool

Using this boolean vector we can select only the rows that met the condition by using the loc method.

df.loc[df['ALT'].apply(len) >= min_len]

	sample_id	CHROM	POS	ID	REF	ALT	QUAL	FILTER	INDEL	IDV	...	ICB	HOB	AC	AN	DP4	MQ	Indiv	gt_PL	gt_GT	gt_GT_alleles
8	SRR2584863	CP000819.1	2103887	NaN	ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG	ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCC...	56.0000	NaN	True	2.0	...	NaN	NaN	1	1	0,1,1,1	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	111,28	1	ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCC...
60	SRR2584866	CP000819.1	197681	NaN	CTTTTTTTT	CTTTTTTTTT	50.0000	NaN	True	13.0	...	NaN	NaN	1	1	0,0,9,4	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	80,0	1	CTTTTTTTTT
72	SRR2584866	CP000819.1	294206	NaN	TGGGGGGG	TGGGGGGGGG	100.0000	NaN	True	8.0	...	NaN	NaN	1	1	0,2,4,4	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	151,24	1	TGGGGGGGGG
78	SRR2584866	CP000819.1	352369	NaN	AGGGGGGGG	AGGGGGGGGG	36.2447	NaN	True	16.0	...	NaN	NaN	1	1	1,1,12,3	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	63,0	1	AGGGGGGGGG
197	SRR2584866	CP000819.1	1021771	NaN	GTTTTTTTT	GTTTTTTTTTT	51.0000	NaN	True	5.0	...	NaN	NaN	1	1	0,1,1,4	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	85,7	1	GTTTTTTTTTT
214	SRR2584866	CP000819.1	1112067	NaN	TCCCCCCC	TCCCCCCCCC	46.4187	NaN	True	4.0	...	NaN	NaN	1	1	0,0,3,1	49	/home/dcuser/dc_workshop/results/bam/SRR258486...	78,2	1	TCCCCCCCCC
237	SRR2584866	CP000819.1	1172377	NaN	TCCCCCC	TCCCCCCCCC	225.0000	NaN	True	8.0	...	NaN	NaN	1	1	0,0,5,3	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	255,0	1	TCCCCCCCCC
315	SRR2584866	CP000819.1	1699806	NaN	CATGATGATGATGAT	CATGATGATGAT	153.0000	NaN	True	6.0	...	NaN	NaN	1	1	1,2,4,2	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	255,75	1	CATGATGATGAT
402	SRR2584866	CP000819.1	2247096	NaN	TCCCCCCC	TCCCCCCCCCC	17.8332	NaN	True	2.0	...	NaN	NaN	1	1	1,0,0,2	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	70,25	1	TCCCCCCCCCC
493	SRR2584866	CP000819.1	2693113	NaN	GTTCTTCTTCTTC	GTTCTTCTTC	213.0000	NaN	True	7.0	...	NaN	NaN	1	1	0,1,4,3	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	255,15	1	GTTCTTCTTC
516	SRR2584866	CP000819.1	2788797	NaN	CAAAAAAAAA	CAAAAAAAAAA	59.0000	NaN	True	14.0	...	NaN	NaN	1	1	0,0,6,8	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	89,0	1	CAAAAAAAAAA
548	SRR2584866	CP000819.1	3052305	NaN	AGCGCGCGCGCG	AGCGCGCGCG	228.0000	NaN	True	9.0	...	NaN	NaN	1	1	0,1,3,5	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	255,0	1	AGCGCGCGCG
722	SRR2584866	CP000819.1	4022380	NaN	GTTTTTTTT	GTTTTTTTTT	11.1284	NaN	True	9.0	...	NaN	NaN	1	1	2,2,3,4	60	/home/dcuser/dc_workshop/results/bam/SRR258486...	39,1	1	GTTTTTTTTT
794	SRR2589044	CP000819.1	2103887	NaN	ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG	ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG	225.0000	NaN	True	10.0	...	NaN	NaN	1	1	0,0,1,9	60	/home/dcuser/dc_workshop/results/bam/SRR258904...	255,0	1	ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG

14 rows × 29 columns

BioPython

BioPython is another packages that Python includes. It features include parsers for various Bioinformatics file formats (BLAST, Clustalw, FASTA, Genbank,…), access to online services (NCBI, Expasy,…), interfaces to common and not-so-common programs (Clustalw, DSSP, MSMS…), a standard sequence class, various clustering modules, a KD tree data structure etc. and even documentation.

To import BioPython Package

For this exersise we are going to import two BioPython (or simply Bio) sub-modules Seq and pairwise2.

from Bio import Seq, pairwise2

The Seq module has a sequence object that stores sequence data in memory more efficiently than strings.

The pairwise2 module has functions that can be used to compare sequences.

Gathering sequences from the dataset we loaded using pandas

Select one of the rows found by the filter and extract the reference and the alter sequences.

row = 8
ref = Seq.Seq(df.loc[row]['REF'])
alt = Seq.Seq(df.loc[row]['ALT'])

print(ref)
print(alt)

ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG

Auto Generate Alignments between the reference and the altered sequence

We can generate alignments between the reference and alter sequences using the pairwise2.align.globalxx function.

alignments = pairwise2.align.globalxx(ref,alt)
print(len(alignments))

To visualize this alignments, pairwise2 provides the format_alignment function that needs to receive multiple parameters. Thankfully

for alignment in alignments:
    print(pairwise2.format_alignment(*alignment))

ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG------------------------
||||||||||||||||||||||||||||||||                        
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCA----G--------------------
|||||||||||||||||||||||||||||||    |                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAGCCAGCC----AG--------------------
||||||||||||||||||||||||||||||    ||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAGCCAGC---C-AG--------------------
|||||||||||||||||||||||||||||   | ||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAGCCAG-C--C-AG--------------------
|||||||||||||||||||||||||||| |  | ||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAGCCAGC----CAG--------------------
|||||||||||||||||||||||||||||    |||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAGCCAG-C---CAG--------------------
|||||||||||||||||||||||||||| |   |||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAGCCAG----CCAG--------------------
||||||||||||||||||||||||||||    ||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAGCCA----GCCAG--------------------
|||||||||||||||||||||||||||    |||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAGCC----AGCCAG--------------------
||||||||||||||||||||||||||    ||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAGC---C-AGCCAG--------------------
|||||||||||||||||||||||||   | ||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAG-C--C-AGCCAG--------------------
|||||||||||||||||||||||| |  | ||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAGC----CAGCCAG--------------------
|||||||||||||||||||||||||    |||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAG-C---CAGCCAG--------------------
|||||||||||||||||||||||| |   |||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCAG----CCAGCCAG--------------------
||||||||||||||||||||||||    ||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCCA----GCCAGCCAG--------------------
|||||||||||||||||||||||    |||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGCC----AGCCAGCCAG--------------------
||||||||||||||||||||||    ||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGC---C-AGCCAGCCAG--------------------
|||||||||||||||||||||   | ||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAG-C--C-AGCCAGCCAG--------------------
|||||||||||||||||||| |  | ||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAGC----CAGCCAGCCAG--------------------
|||||||||||||||||||||    |||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAG-C---CAGCCAGCCAG--------------------
|||||||||||||||||||| |   |||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCAG----CCAGCCAGCCAG--------------------
||||||||||||||||||||    ||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCCA----GCCAGCCAGCCAG--------------------
|||||||||||||||||||    |||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGCC----AGCCAGCCAGCCAG--------------------
||||||||||||||||||    ||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGC---C-AGCCAGCCAGCCAG--------------------
|||||||||||||||||   | ||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAG-C--C-AGCCAGCCAGCCAG--------------------
|||||||||||||||| |  | ||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAGC----CAGCCAGCCAGCCAG--------------------
|||||||||||||||||    |||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAG-C---CAGCCAGCCAGCCAG--------------------
|||||||||||||||| |   |||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCAG----CCAGCCAGCCAGCCAG--------------------
||||||||||||||||    ||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCCA----GCCAGCCAGCCAGCCAG--------------------
|||||||||||||||    |||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGCC----AGCCAGCCAGCCAGCCAG--------------------
||||||||||||||    ||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGC---C-AGCCAGCCAGCCAGCCAG--------------------
|||||||||||||   | ||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAG-C--C-AGCCAGCCAGCCAGCCAG--------------------
|||||||||||| |  | ||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAGC----CAGCCAGCCAGCCAGCCAG--------------------
|||||||||||||    |||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAG-C---CAGCCAGCCAGCCAGCCAG--------------------
|||||||||||| |   |||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCAG----CCAGCCAGCCAGCCAGCCAG--------------------
||||||||||||    ||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCCA----GCCAGCCAGCCAGCCAGCCAG--------------------
|||||||||||    |||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGCC----AGCCAGCCAGCCAGCCAGCCAG--------------------
||||||||||    ||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGC---C-AGCCAGCCAGCCAGCCAGCCAG--------------------
|||||||||   | ||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAG-C--C-AGCCAGCCAGCCAGCCAGCCAG--------------------
|||||||| |  | ||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAGC----CAGCCAGCCAGCCAGCCAGCCAG--------------------
|||||||||    |||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAG-C---CAGCCAGCCAGCCAGCCAGCCAG--------------------
|||||||| |   |||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCAG----CCAGCCAGCCAGCCAGCCAGCCAG--------------------
||||||||    ||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCCA----GCCAGCCAGCCAGCCAGCCAGCCAG--------------------
|||||||    |||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGCC----AGCCAGCCAGCCAGCCAGCCAGCCAG--------------------
||||||    ||||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGC---C-AGCCAGCCAGCCAGCCAGCCAGCCAG--------------------
|||||   | ||||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAG-C--C-AGCCAGCCAGCCAGCCAGCCAGCCAG--------------------
|||| |  | ||||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAGC----CAGCCAGCCAGCCAGCCAGCCAGCCAG--------------------
|||||    |||||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAG-C---CAGCCAGCCAGCCAGCCAGCCAGCCAG--------------------
|||| |   |||||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACAG----CCAGCCAGCCAGCCAGCCAGCCAGCCAG--------------------
||||    ||||||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

ACA----GCCAGCCAGCCAGCCAGCCAGCCAGCCAG--------------------
|||    |||||||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

AC----AGCCAGCCAGCCAGCCAGCCAGCCAGCCAG--------------------
||    ||||||||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

A---C-AGCCAGCCAGCCAGCCAGCCAGCCAGCCAG--------------------
|   | ||||||||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

--A-C-AGCCAGCCAGCCAGCCAGCCAGCCAGCCAG--------------------
  | | ||||||||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

A----CAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG--------------------
|    |||||||||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32

--A--CAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG--------------------
  |  |||||||||||||||||||||||||||||||                    
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
  Score=32