quantifying counts per transcript - no gtf file

[Lucila Traverso]

Hello,

first of all, thank you for the support for using STAR. I consider this is a great mapping software.

I have some doubts regarding the use of STAR on my data. I have used Trinity for assembling a de novo transcriptome of my non-model species (with no genome available). The main objective of my experiment is to perform differential expression analysis and I would like to try STAR for the mapping step. I have already indexed my de novo assembly with the following command:

STAR --runMode genomeGenerate --genomeDir assembly_INDEX/ --genomeFastaFiles transcriptome.fasta --runThreadN 20 

Since it is a transcriptome (resulting from a de novo assembly), no gft file was included. So, as I understand, I can't use --quantMode in the mapping step, and I have used the following command for mapping (example for 1 sample):

STAR --runMode alignReads --runThreadN 2 --genomeDir assembly_INDEX/ --readFilesIn C1.paired.1.fastq.gz C1.paired.2.fastq.gz --readFilesCommand gunzip -c --bamRemoveDuplicatesType UniqueIdentical --outFilterMultimapNmax 1 --outBAMsortingThreadN 6 --outSAMtype BAM SortedByCoordinate --outFileNamePrefix C1/

and obtained Aligned.sortedByCoord.out.bam, Log.final.out, Log.out, Log.progress.out and SJ.out.tab output files. 

So, my question is: is this approach correct to obtain "counts per transcript", I mean, the number of reads that map to each transcript in my de novo assembly (similar to what I obtain for each gene with --quantMode GeneCounts when I use a genome with a GTF)? If it is, how can I obtain them, in order to use them to perform EdgeR or DESeq2 analysis?

I have used version 2.7.1a.

Any suggestion/advice is welcome.

Thank you in advance.

Lucila

[Alexander Dobin]

Hi Lucila,

you are mapping to Trinity generated transcripts, so, in principle, you can count reads that map to each of the transcripts.

However, a large portion of the reads will be multimappers, i.e. mapping equally well to different transcripts, so you cannot simply count them.

The best approach is to use a "quantifier" tool such as RSEM - you can feed the BAM files that you obtained.

It will generate transcript TPM and counts, "resolving" the multimapping reads.

Cheers

Alex

[Lucila Traverso]

Dear Alex,

thank you so much for your kind response, I will try your suggestions.

Basing on your response, I have another question: is the RSEM step always required for this kind of data? Or does it depend on the mapping results? I am asking this question because when I map against the whole assembly I can see, as you explained, a high multimapping rate in the results. However, I have run some tests with filtered assemblies (for example, mapping only against the non-redundant cds) and found that the "% of reads mapped to too many loci" is below 10%. Taking this into account, do you think it is recommendable to use RSEM in that case, too?

Best,

Lucila.

[Alexander Dobin]

Hi Lucila,

you also need to check the "% of reads mapped to multiple loci" - those are the multimappers that RSEM will "resolve".

The "% of reads mapped to too many loci" are those reads that mapped to more than --outFilterMultimapNmax (default=10) loci, and are not output.

10% is on the large side - I would increase the --outFilterMultimapNmax to ~30 or 50 to output such reads, they will be multimappers, of course.

Note that "transcriptomic" multimappers arise from both alternative isoforms of the same gene, and from paralogous genes.

Because of that RSEM is a generally recommended approach unless you can (and want to) filter your transcripts down to 1 isoform per gene family.

Cheers

Alex

[Lucila Traverso]

Thank you so much Alex, you were very helpful. I will try your suggested approach.

Best,

Lucila.

[Lucila Traverso]

Hi Alex,
I've been trying to use the .bam files obtained with STAR in RSEM, but I am having some trouble. As I've told you in my previous messages, I have indexed my de novo transcriptome with STAR (without gff) and aligned using the following command:

$ STAR --runMode alignReads --runThreadN 2 --genomeDir transcriptome_index/ --readFilesIn C1.paired.1.fastq.gz C1.paired.2.fastq.gz --readFilesCommand gunzip -c --outFilterMultimapNmax 50 --outFilterScoreMinOverLread 0.3 --outFilterMatchNminOverLread 0.3 --outSAMtype BAM SortedByCoordinate --outFileNamePrefix sample_C1/

I checked the bam file with "rsem-sam-validator" and it is not suitable for RSEM. The error is:

$ rsem-sam-validator Aligned.sortedByCoord.out.bam
.
Only find one mate for paired-end read K00124:558:HFKJWBBXX:7:1101:22690:35774!
Please make sure that the two mates of a paired-end read are adjacent to each other.
The input file is not valid!

So I tried to format it with "convert-sam-for-rsem". The result is not ok, since "rsem-sam-validator" says:

Clipping or padding is detected (cigar S) for read K00124:558:HFKJWBBXX:7:1101:1010:6379!
RSEM currently doest not support clipping or padding.
The input file is not valid!

Lastly, I have tried sorting the file with:

$ samtools sort -n -@ 1 -m 1G -o Aligned.sortedByCoord.out.forRSEM.bam Aligned.sortedByCoord.out.bam

But I also obtained an unvalid result; "rsem-sam-validator" says: "The two mates of paired-end read K00124:558:HFKJWBBXX:7:1101:1010:6379 are marked as both mate1 or both mate2! The input file is not valid!"

If I try to run RSEM with those files I obtain different errors. If I try with the bam obtained from STAR, I obtain:

Read K00124:558:HFKJWBBXX:7:1126:4501:44775: The adjacent two lines do not represent the two mates of a paired-end read! (RSEM assumes the two mates of a paired-end read should be adjacent)

If I try with the result ofsamtools sort -n, i obtain:

Read K00124:558:HFKJWBBXX:7:1101:1010:6379: The adjacent two lines do not represent the two mates of a paired-end read! (RSEM assumes the two mates of a paired-end read should be adjacent)

If I try with the result of "convert-sam-for-rsem", I obtain:

Read K00124:558:HFKJWBBXX:7:1101:1010:6379: RSEM currently does not support gapped alignments, sorry!

When I visualized one of those reads in the .bam file, this is what I obtained for the 'original' bam:

$ samtools view Aligned.sortedByCoord.out.bam | grep 'K00124:558:HFKJWBBXX:7:1101:1010:6379'
K00124:558:HFKJWBBXX:7:1101:1010:6379    419    TRINITY_DN1347_c0_g1_i21.p2    32    1    46S104M    =    161    279    CAGGAACACATCTCATAAAACAACATGTTGAAATTGTTCGTCCTTTCAGTGCTCGTAGCAGCCGCGTCCTCTGCTGGACTTGGCGGACTCGGGGCTGTTTATGGAGCTGGCTATGGTTATGGTTACCATGCCCCATTGACTGTAGCCCAC    AAFFFJJJJJJJFJJJJFJJJJJJJFJJJFJJJFJJJJJJJFJJFJJJFFJJFAJJJFFFJJJJJFAJFJJJJJJJJJJJFJFJ7FFJJJJJJJJAAJJJJJJJJFJFJ7JJJJJJJFFFJJJFJJJJJFF<AJFJJJ<FJJJJJJ)AJF    NH:i:3    HI:i:3    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    339    TRINITY_DN1347_c0_g1_i21.p2    161    1    150M    =    32    -279    CAGTCACCACCGTCGCCCATGCTCCAGTAGCTACCGTCGCCCATGCTCCAGTAACCGGTGTCCGGGCAGTTTATGGAGCTGGTTATGGTTATGGTTACAATGCGCCATTGACTGTAGCACACACACCAGCTGTAAGTTATGCTGCTCCAG    F-JFJFJF<JJJJJJJJJFFJFFFJJJJJJFFJJJJJJJJJJFJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJAFAJJJJJJFJFJJJJJJJJJJJJJFJJJJFJJJJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJFJJJFJJJFFFAA    NH:i:3    HI:i:3    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    419    TRINITY_DN1347_c0_g1_i45.p2    32    1    46S104M    =    161    279    CAGGAACACATCTCATAAAACAACATGTTGAAATTGTTCGTCCTTTCAGTGCTCGTAGCAGCCGCGTCCTCTGCTGGACTTGGCGGACTCGGGGCTGTTTATGGAGCTGGCTATGGTTATGGTTACCATGCCCCATTGACTGTAGCCCAC    AAFFFJJJJJJJFJJJJFJJJJJJJFJJJFJJJFJJJJJJJFJJFJJJFFJJFAJJJFFFJJJJJFAJFJJJJJJJJJJJFJFJ7FFJJJJJJJJAAJJJJJJJJFJFJ7JJJJJJJFFFJJJFJJJJJFF<AJFJJJ<FJJJJJJ)AJF    NH:i:3    HI:i:2    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    339    TRINITY_DN1347_c0_g1_i45.p2    161    1    150M    =    32    -279    CAGTCACCACCGTCGCCCATGCTCCAGTAGCTACCGTCGCCCATGCTCCAGTAACCGGTGTCCGGGCAGTTTATGGAGCTGGTTATGGTTATGGTTACAATGCGCCATTGACTGTAGCACACACACCAGCTGTAAGTTATGCTGCTCCAG    F-JFJFJF<JJJJJJJJJFFJFFFJJJJJJFFJJJJJJJJJJFJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJAFAJJJJJJFJFJJJJJJJJJJJJJFJJJJFJJJJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJFJJJFJJJFFFAA    NH:i:3    HI:i:2    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    163    TRINITY_DN1347_c0_g1_i49.p2    32    1    46S104M    =    161    279    CAGGAACACATCTCATAAAACAACATGTTGAAATTGTTCGTCCTTTCAGTGCTCGTAGCAGCCGCGTCCTCTGCTGGACTTGGCGGACTCGGGGCTGTTTATGGAGCTGGCTATGGTTATGGTTACCATGCCCCATTGACTGTAGCCCAC    AAFFFJJJJJJJFJJJJFJJJJJJJFJJJFJJJFJJJJJJJFJJFJJJFFJJFAJJJFFFJJJJJFAJFJJJJJJJJJJJFJFJ7FFJJJJJJJJAAJJJJJJJJFJFJ7JJJJJJJFFFJJJFJJJJJFF<AJFJJJ<FJJJJJJ)AJF    NH:i:3    HI:i:1    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    83    TRINITY_DN1347_c0_g1_i49.p2    161    1    150M    =    32    -279    CAGTCACCACCGTCGCCCATGCTCCAGTAGCTACCGTCGCCCATGCTCCAGTAACCGGTGTCCGGGCAGTTTATGGAGCTGGTTATGGTTATGGTTACAATGCGCCATTGACTGTAGCACACACACCAGCTGTAAGTTATGCTGCTCCAG    F-JFJFJF<JJJJJJJJJFFJFFFJJJJJJFFJJJJJJJJJJFJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJAFAJJJJJJFJFJJJJJJJJJJJJJFJJJJFJJJJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJFJJJFJJJFFFAA    NH:i:3    HI:i:1    AS:i:248    nM:i:2


For the sorted bam (with samtools sort -n):

$ samtools view Aligned.sortedByCoord.out.forRSEM.bam | grep 'K00124:558:HFKJWBBXX:7:1101:1010:6379'

K00124:558:HFKJWBBXX:7:1101:1010:6379    339    TRINITY_DN1347_c0_g1_i21.p2    161    1    150M    =    32    -279    CAGTCACCACCGTCGCCCATGCTCCAGTAGCTACCGTCGCCCATGCTCCAGTAACCGGTGTCCGGGCAGTTTATGGAGCTGGTTATGGTTATGGTTACAATGCGCCATTGACTGTAGCACACACACCAGCTGTAAGTTATGCTGCTCCAG    F-JFJFJF<JJJJJJJJJFFJFFFJJJJJJFFJJJJJJJJJJFJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJAFAJJJJJJFJFJJJJJJJJJJJJJFJJJJFJJJJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJFJJJFJJJFFFAA    NH:i:3    HI:i:3    AS:i:248    nM:i:2

K00124:558:HFKJWBBXX:7:1101:1010:6379    339    TRINITY_DN1347_c0_g1_i45.p2    161    1    150M    =    32    -279    CAGTCACCACCGTCGCCCATGCTCCAGTAGCTACCGTCGCCCATGCTCCAGTAACCGGTGTCCGGGCAGTTTATGGAGCTGGTTATGGTTATGGTTACAATGCGCCATTGACTGTAGCACACACACCAGCTGTAAGTTATGCTGCTCCAG    F-JFJFJF<JJJJJJJJJFFJFFFJJJJJJFFJJJJJJJJJJFJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJAFAJJJJJJFJFJJJJJJJJJJJJJFJJJJFJJJJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJFJJJFJJJFFFAA    NH:i:3    HI:i:2    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    83    TRINITY_DN1347_c0_g1_i49.p2    161    1    150M    =    32    -279    CAGTCACCACCGTCGCCCATGCTCCAGTAGCTACCGTCGCCCATGCTCCAGTAACCGGTGTCCGGGCAGTTTATGGAGCTGGTTATGGTTATGGTTACAATGCGCCATTGACTGTAGCACACACACCAGCTGTAAGTTATGCTGCTCCAG    F-JFJFJF<JJJJJJJJJFFJFFFJJJJJJFFJJJJJJJJJJFJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJAFAJJJJJJFJFJJJJJJJJJJJJJFJJJJFJJJJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJFJJJFJJJFFFAA    NH:i:3    HI:i:1    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    419    TRINITY_DN1347_c0_g1_i21.p2    32    1    46S104M    =    161    279    CAGGAACACATCTCATAAAACAACATGTTGAAATTGTTCGTCCTTTCAGTGCTCGTAGCAGCCGCGTCCTCTGCTGGACTTGGCGGACTCGGGGCTGTTTATGGAGCTGGCTATGGTTATGGTTACCATGCCCCATTGACTGTAGCCCAC    AAFFFJJJJJJJFJJJJFJJJJJJJFJJJFJJJFJJJJJJJFJJFJJJFFJJFAJJJFFFJJJJJFAJFJJJJJJJJJJJFJFJ7FFJJJJJJJJAAJJJJJJJJFJFJ7JJJJJJJFFFJJJFJJJJJFF<AJFJJJ<FJJJJJJ)AJF    NH:i:3    HI:i:3    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    419    TRINITY_DN1347_c0_g1_i45.p2    32    1    46S104M    =    161    279    CAGGAACACATCTCATAAAACAACATGTTGAAATTGTTCGTCCTTTCAGTGCTCGTAGCAGCCGCGTCCTCTGCTGGACTTGGCGGACTCGGGGCTGTTTATGGAGCTGGCTATGGTTATGGTTACCATGCCCCATTGACTGTAGCCCAC    AAFFFJJJJJJJFJJJJFJJJJJJJFJJJFJJJFJJJJJJJFJJFJJJFFJJFAJJJFFFJJJJJFAJFJJJJJJJJJJJFJFJ7FFJJJJJJJJAAJJJJJJJJFJFJ7JJJJJJJFFFJJJFJJJJJFF<AJFJJJ<FJJJJJJ)AJF    NH:i:3    HI:i:2    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    163    TRINITY_DN1347_c0_g1_i49.p2    32    1    46S104M    =    161    279    CAGGAACACATCTCATAAAACAACATGTTGAAATTGTTCGTCCTTTCAGTGCTCGTAGCAGCCGCGTCCTCTGCTGGACTTGGCGGACTCGGGGCTGTTTATGGAGCTGGCTATGGTTATGGTTACCATGCCCCATTGACTGTAGCCCAC    AAFFFJJJJJJJFJJJJFJJJJJJJFJJJFJJJFJJJJJJJFJJFJJJFFJJFAJJJFFFJJJJJFAJFJJJJJJJJJJJFJFJ7FFJJJJJJJJAAJJJJJJJJFJFJ7JJJJJJJFFFJJJFJJJJJFF<AJFJJJ<FJJJJJJ)AJF    NH:i:3    HI:i:1    AS:i:248    nM:i:2

For the sorted bam (with "convert-sam-for-rsem"):

$ samtools view Aligned.sortedByCoord.out.forRSEMconv.bam.tmp.bam | grep 'K00124:558:HFKJWBBXX:7:1101:1010:6379'

K00124:558:HFKJWBBXX:7:1101:1010:6379    419    TRINITY_DN1347_c0_g1_i21.p2    32    1    46S104M    =    161    279    CAGGAACACATCTCATAAAACAACATGTTGAAATTGTTCGTCCTTTCAGTGCTCGTAGCAGCCGCGTCCTCTGCTGGACTTGGCGGACTCGGGGCTGTTTATGGAGCTGGCTATGGTTATGGTTACCATGCCCCATTGACTGTAGCCCAC    AAFFFJJJJJJJFJJJJFJJJJJJJFJJJFJJJFJJJJJJJFJJFJJJFFJJFAJJJFFFJJJJJFAJFJJJJJJJJJJJFJFJ7FFJJJJJJJJAAJJJJJJJJFJFJ7JJJJJJJFFFJJJFJJJJJFF<AJFJJJ<FJJJJJJ)AJF    NH:i:3    HI:i:3    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    339    TRINITY_DN1347_c0_g1_i21.p2    161    1    150M    =    32    -279    CAGTCACCACCGTCGCCCATGCTCCAGTAGCTACCGTCGCCCATGCTCCAGTAACCGGTGTCCGGGCAGTTTATGGAGCTGGTTATGGTTATGGTTACAATGCGCCATTGACTGTAGCACACACACCAGCTGTAAGTTATGCTGCTCCAG    F-JFJFJF<JJJJJJJJJFFJFFFJJJJJJFFJJJJJJJJJJFJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJAFAJJJJJJFJFJJJJJJJJJJJJJFJJJJFJJJJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJFJJJFJJJFFFAA    NH:i:3    HI:i:3    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    419    TRINITY_DN1347_c0_g1_i45.p2    32    1    46S104M    =    161    279    CAGGAACACATCTCATAAAACAACATGTTGAAATTGTTCGTCCTTTCAGTGCTCGTAGCAGCCGCGTCCTCTGCTGGACTTGGCGGACTCGGGGCTGTTTATGGAGCTGGCTATGGTTATGGTTACCATGCCCCATTGACTGTAGCCCAC    AAFFFJJJJJJJFJJJJFJJJJJJJFJJJFJJJFJJJJJJJFJJFJJJFFJJFAJJJFFFJJJJJFAJFJJJJJJJJJJJFJFJ7FFJJJJJJJJAAJJJJJJJJFJFJ7JJJJJJJFFFJJJFJJJJJFF<AJFJJJ<FJJJJJJ)AJF    NH:i:3    HI:i:2    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    339    TRINITY_DN1347_c0_g1_i45.p2    161    1    150M    =    32    -279    CAGTCACCACCGTCGCCCATGCTCCAGTAGCTACCGTCGCCCATGCTCCAGTAACCGGTGTCCGGGCAGTTTATGGAGCTGGTTATGGTTATGGTTACAATGCGCCATTGACTGTAGCACACACACCAGCTGTAAGTTATGCTGCTCCAG    F-JFJFJF<JJJJJJJJJFFJFFFJJJJJJFFJJJJJJJJJJFJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJAFAJJJJJJFJFJJJJJJJJJJJJJFJJJJFJJJJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJFJJJFJJJFFFAA    NH:i:3    HI:i:2    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    163    TRINITY_DN1347_c0_g1_i49.p2    32    1    46S104M    =    161    279    CAGGAACACATCTCATAAAACAACATGTTGAAATTGTTCGTCCTTTCAGTGCTCGTAGCAGCCGCGTCCTCTGCTGGACTTGGCGGACTCGGGGCTGTTTATGGAGCTGGCTATGGTTATGGTTACCATGCCCCATTGACTGTAGCCCAC    AAFFFJJJJJJJFJJJJFJJJJJJJFJJJFJJJFJJJJJJJFJJFJJJFFJJFAJJJFFFJJJJJFAJFJJJJJJJJJJJFJFJ7FFJJJJJJJJAAJJJJJJJJFJFJ7JJJJJJJFFFJJJFJJJJJFF<AJFJJJ<FJJJJJJ)AJF    NH:i:3    HI:i:1    AS:i:248    nM:i:2
K00124:558:HFKJWBBXX:7:1101:1010:6379    83    TRINITY_DN1347_c0_g1_i49.p2    161    1    150M    =    32    -279    CAGTCACCACCGTCGCCCATGCTCCAGTAGCTACCGTCGCCCATGCTCCAGTAACCGGTGTCCGGGCAGTTTATGGAGCTGGTTATGGTTATGGTTACAATGCGCCATTGACTGTAGCACACACACCAGCTGTAAGTTATGCTGCTCCAG    F-JFJFJF<JJJJJJJJJFFJFFFJJJJJJFFJJJJJJJJJJFJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJAFAJJJJJJFJFJJJJJJJJJJJJJFJJJJFJJJJJJJJJJJJJJJJJJFJFJJJJJJJJJJJJFJJJFJJJFFFAA    NH:i:3    HI:i:1    AS:i:248    nM:i:2

I also paste here the reads in the fastq files:

$ zgrep -A3 'K00124:558:HFKJWBBXX:7:1101:1010:6379' C1.paired.1.fastq.gz
@K00124:558:HFKJWBBXX:7:1101:1010:6379 1:N:0:AACTCACC
CTGGAGCAGCATAACTTACAGCTGGTGTGTGTGCTACAGTCAATGGCGCATTGTAACCATAACCATAACCAGCTCCATAAACTGCCCGGACACCGGTTACTGGAGCATGGGCGACGGTAGCTACTGGAGCATGGGCGACGGTGGTGACTG
+
AAFFFJJJFJJJFJJJJJJJJJJJJFJFJJJJJJJJJJJJJJJJJJFJJJJFJJJJJJJJJJJJJFJFJJJJJJAFAJJJJJJJJJJJJFJFJJJJJJJJJJJJJJJFJJJJJJJJJJFFJJJJJJFFFJFFJJJJJJJJJ<FJFJFJ-F

$ zgrep -A3 'K00124:558:HFKJWBBXX:7:1101:1010:6379' C1.paired.2.fastq.gz
@K00124:558:HFKJWBBXX:7:1101:1010:6379 2:N:0:AACTCACC
CAGGAACACATCTCATAAAACAACATGTTGAAATTGTTCGTCCTTTCAGTGCTCGTAGCAGCCGCGTCCTCTGCTGGACTTGGCGGACTCGGGGCTGTTTATGGAGCTGGCTATGGTTATGGTTACCATGCCCCATTGACTGTAGCCCAC
+
AAFFFJJJJJJJFJJJJFJJJJJJJFJJJFJJJFJJJJJJJFJJFJJJFFJJFAJJJFFFJJJJJFAJFJJJJJJJJJJJFJFJ7FFJJJJJJJJAAJJJJJJJJFJFJ7JJJJJJJFFFJJJFJJJJJFF<AJFJJJ<FJJJJJJ)AJF


Do you have any advice to overcome this problem? Any help is welcome.
Thank you in advance and sorry for all the questions,
Lucila.