Check out proovread. It ..

• .. outperforms PacBioToCA/LSC in terms of accuracy and contiguity/sensitivity
• .. is easy to install/run/configure
• .. supports various types of dat
  • HiSeq/MiSeq 100-500bp)
  • **Unitigs
  • 454, ...

proovread maps high coverage data to pacbio reads (bwa mem, blasr, daligner) in multiple iterations. This is not the fastest (if speed is your concern, go for LoRDEC http://www.atgc-montpellier.fr/lordec/) but the most thorough approach, giving you the most out of your PacBio data. You find some comparative stats here.

I've used PBcR in the Celera Assembler package.

It works for hybrid assembly as well as just PacBio assembly (actually, I found self-correction worked better than hybrid correction with the dataset that I worked with)

If you haven't seen them already, I would also recommend viewing the tutorials on the PacBio website.

I think there is at least 1-2 talks that review methods for de novo assembly and read correction.

There is "LoRDEC: accurate and efficient long read error correction", it uses DBG short reads to correct erroneous parts in Long reads.

It's a new program for correcting Long rads.

ECTools is the one that has worked best for me. It's written for a particular kind of grid computing system though, so you may have to modify step 8 from their tutorial to suite your particular environment.

For running on a single server (which will be pretty slow, but this is just an example of how to wrap their scripts for a different scheduling system) I used the following bash script instead of steps 8 and 9

#! /bin/bash
export TMPDIR=/a/directory/for/temporary_files
mkdir -p $TMPDIR
THREADS=12
NUM_PARTITIONS=0213 # should be a 4 character wide integer left-padded with zeros
NUM_FILES_PER_PARTITION=500
ORGANISM_NAME=organism_name

run_file() {
        export SGE_TASK_ID=$1
        ../correct.sh
}
export -f run_file

for i in `eval echo {0001..$NUM_PARTITIONS}` #braces evaluated before variable
do
 echo $i
        cd $i
        parallel -j $THREADS run_file ::: `eval echo {1..$NUM_FILES_PER_PARTITION}`
        cd ..
done
cat ????/*.cor.fa > ${ORGANISM_NAME}.cor.fa

PacBioToCA (error correction via Celera Assembler) can also be used for error correcting PacBio reads using short reads including Illumina's.

Another one is LSC

I am very happy with proovread.

It is extremely flexible with respect to the type of Illumina data (HiSeqs, MiSeq, unitigs etc), quite fast, completely tunable and the author (Thomas Hackl) is very responsive. We have used it to correct lots of PacBios and it is extremly stable.

In contrast to ECTools, which takes much much longer and gives cluster jobs with unpredictable runtimes (depending on how many repeats the PacBio reads have), proovread jobs have a predictable runtime with little variation, which makes it easy to tailor jobs to the requirements of a compute cluster (runtimes, # cores etc). Memory usage is minimal.

I used proovread recently to correct long reads by mapping short reads. For my volume I had to use HPC for one week to finnish correction. Author of this soft, Thomas, is very responsive, indeed. I also used pacbioToCA for pacbio self correction having 40x coverage, even though caveat for good performance is 50x. I didn't get satisfying results. With proovread you loose around 25% in pacbio length. With Celera in my case it was around 60%.

If we know the reference genome, why not correct the PacBio Transcriptome data using the transcript annotated from genome directly? The work will become only to align the PacBio reads to the reference transcripts. It seems easy work?