preprocess_radtag_lane_vlbc.py

#!/usr/bin/env python

'''
EXPERIMENTAL VARIABLE-LENGTH BARCODE SUPPORT (20120905) - proceed with caution.

give fastq file(s) (1 or 4 line format)
preprocess_radtag_lane.py /path/to/flowcell/s_N_1_sequence.txt [s_N_2_sequence.txt]

for which individual data is present in <LIBRARY_DATA> gdoc spreadsheet (see config.py)

generates tabular uniqued read data:
ID nreads sequence mean_qual comma,delim,indiv comma,delim,depths_by_ind \
                                  comma,delim,unique,read2,seq comma,delim,unique,read2,counts

NOTES ON FILENAMES:
flowcell is extracted from the name of the directory containing the fastq file(s)
Thus, the path supplied to the fastq file(s) in running preprocess_radtag_lane.py
must include at least the containing folder.  The name of the containing  folder
must match the flowcell designation in <LIBRARY_DATA> gdoc spreadsheet 

'''

import os, sys, re, numpy, gzip
import gdata.spreadsheet.service

from collections import defaultdict
from subprocess import Popen, PIPE
from editdist import distance
from glob import glob
from config import EMAIL,PASS,SOURCE,LIBRARY_DATA,ADAPTER_DATA,RTDROOT

def dezip(values_in):
    '''opposite of zip(), i.e. 
    >>> dezip([("a",1),("b",2),("c",3)])
    (["a","b","c"],[1,2,3])'''

    if isinstance(values_in,tuple):
        values_in = [values_in]
	
    lol = []
    for i in range(len(values_in[0])):
        lol.append([])
    for l in values_in:
        for it,li in zip(l,lol):
	    li.append(it)
    return tuple(lol)


def smartopen(filename,*args,**kwargs):
    '''opens with open unless file ends in .gz, then use gzip.open

    in theory should transparently allow reading of files regardless of compression
    '''
    if filename.endswith('.gz'):
        return gzip.open(filename,*args,**kwargs)
    else:
        return open(filename,*args,**kwargs)


def get_read_count(filename,lnum=None,use_cache=True):

    if use_cache:
        rcc = filename+'.rc.cache'
        try:
            filesize,rc = open(rcc).readline().strip().split()
            if float(filesize) == os.path.getsize(filename):
                print >> sys.stderr, 'read count from cached value: %s' % rc
                return int(rc)
        except:
            pass

    if lnum is None:
        if smartopen(filename).read(1) == '@':
	    lnum = 4
	else:
	    lnum = 1
	
    if filename.endswith('.gz'):    
        print >> sys.stderr, 'getting read count for compressed file',filename,'...',
        rc = int(Popen('gunzip -c %s | wc -l' % filename,shell=True,stdout=PIPE).stdout.read().split()[0]) / lnum
        print >> sys.stderr, rc
    else:
        print >> sys.stderr, 'getting read count for file',filename,'...',
        rc = int(Popen('wc -l %s' % filename,shell=True,stdout=PIPE).stdout.read().split()[0]) / lnum
        print >> sys.stderr, rc

    if use_cache:
        open(rcc,'w').write('%s\t%s\n' % (os.path.getsize(filename),rc))
        
    return rc

def get_baseQ(qstr):
    q = [ord(c) for c in qstr]
    if any([i<66 for i in q]):
        return 33
    elif any([i>74 for i in q]):
        return 64
    else:
        return None

def create_empty_table(table_name):
    try:
        key, gd_client = get_spreadsheet_key(table_name)
        print >> sys.stderr, 'table %s exists, skip' % table_name
    except:
        import gdata.docs.data
        import gdata.docs.client
        client = gdata.docs.client.DocsClient(source=SOURCE)
        client.ssl = True  # Force all API requests through HTTPS
        client.http_client.debug = False  # Set to True for debugging HTTP requests
        
        client.ClientLogin(EMAIL,PASS,client.source)
        
        new_spreadsheet = client.Create(gdata.docs.data.SPREADSHEET_LABEL, table_name , writers_can_invite=False)
        print >> sys.stderr, 'Spreadsheet "%s" created' % new_spreadsheet.title.text


def get_spreadsheet_key(target_sheet,gd_client=None):
    '''returns the key string for a spreadsheet given its name'''

    if gd_client is None:
        gd_client = gdata.spreadsheet.service.SpreadsheetsService()
        gd_client.email = EMAIL
        gd_client.password = PASS
        gd_client.source = SOURCE

    gd_client.ProgrammaticLogin()

    feed = gd_client.GetSpreadsheetsFeed()
    key = [entry.id.text.rsplit('/', 1)[1] for entry in feed.entry if entry.title.text == target_sheet][0]

    return key,gd_client

def get_table_as_dict(target_sheet,sq=None,gd_client=None,suppress_fc_check=False):

    key,gd_client = get_spreadsheet_key(target_sheet,gd_client)
    if sq is not None:
        q = gdata.spreadsheet.service.ListQuery()
        q.sq = sq
        feed = gd_client.GetListFeed(key,query=q)
    else:
        feed = gd_client.GetListFeed(key)

    recs = []
    for entry in feed.entry:
        #d = []
	#for el in entry.content.text.split(','):
	    
        try:
	    recs.append(dict(re.findall('(.+?):\s(.+?)(?:(?:,\s)|$)',entry.content.text)))
	    if not suppress_fc_check and not all([k in recs[-1].keys() for k in ['flowcell','lane','pool']]):
	        print >> sys.stderr, 'missing keys:', dict(re.findall('(.+?):\s(.+?)(?:(?:,\s)|$)',entry.content.text))
	        print >> sys.stderr, 'line was:\n',entry.content.text
	except:
	    print >> sys.stderr, 'invalid:', entry.content.text#.split(',')

    return recs

def get_adapter_index_lookup(verbose=True):
    '''returns dict of dicts:
    { <adaptersversion> : { <well> : <index_seq> } }
    '''
    
    key,gd_client = get_spreadsheet_key(ADAPTER_DATA)
    feed = gd_client.GetListFeed(key)

    d = []
    for entry in feed.entry:
        tl = [[st.strip() for st in el.split(':')] for el in entry.content.text.split(',')]
        try:
            d.append(dict(tl))
        except:
            print >> sys.stderr, 'tuple list did not parse:\n\t%s' % tl

    idxlookup = defaultdict(dict)
    for el in d:
        idxlookup[el['adaptersversion']].update({el['well']:el['idx']})
    if verbose==True:
        print >> sys.stderr, 'loaded adapter lookup from %s lines in %s' % (len(d),ADAPTER_DATA) 
    return idxlookup

def get_individual_data_for_lane(filename=None,idxlookup=None,fc=None,lane=None,index=None):
    '''given a fastq file, treats the directory immediately above as the flowcell ID, returns dict:
    { <sequence_index_tag> : **ROW_FROM_LIBRARY_DATA } 
    '''
    if idxlookup is None:
        idxlookup = get_adapter_index_lookup()

    if fc is None and lane is None:
        fc = os.path.basename(os.path.dirname(filename))
        lane = os.path.basename(filename)[2]
        #print >> sys.stderr, fc,lane,idxlookup
        fbase = os.path.basename(filename)

        key,gd_client = get_spreadsheet_key(LIBRARY_DATA)

        q = gdata.spreadsheet.service.ListQuery()
        if 'index' in fbase:
    	    q.sq = 'flowcell="%s" and lane="%s" and index="%s"' % (fc,lane,fbase.split('index')[-1].split('.')[0])
        else:
    	    q.sq = 'flowcell="%s" and lane="%s"' % (fc,lane)
    else:
        key,gd_client = get_spreadsheet_key(LIBRARY_DATA)

        q = gdata.spreadsheet.service.ListQuery()
        if index is not None:
            q.sq = 'flowcell="%s" and lane="%s" and index="%s"' % (fc,lane,index)
        else:
            q.sq = 'flowcell="%s" and lane="%s"' % (fc,lane)



    feed = gd_client.GetListFeed(key,query=q)

    recs = []
    for entry in feed.entry:
        tl = [[st.strip() for st in el.split(':')] for el in entry.content.text.split(',')]
        try:
            recs.append(dict(tl))
        except:
            print >> sys.stderr, 'tuple list did not parse:\n\t%s' % tl

    #recs = [dict([[st.strip() for st in el.split(':')] for el in entry.content.text.split(',')]) for entry in feed.entry]
    print >> sys.stderr, "%s records found for %s" % (len(recs), q.sq)
    adaptersversions = set([r['adaptersversion'] for r in recs])
    print >> sys.stderr, "adapters used: %s" % adaptersversions
    idxs = reduce(lambda x,y: x+y, [idxlookup[adver].values() for adver in adaptersversions])
    idxlens = set([len(idx) for idx in idxs])
    if len(idxlens) != 1:
        #raise ValueError, 'non-uniform index lengths %s for %s' % (idxlens,filename)
        print >> sys.stderr, 'non-uniform index lengths %s for %s; experimental, proceed with caution!' % (idxlens,filename)

    try:
        sampleids = [r['sampleid'] for r in recs]
    except KeyError:
        try: #permit backup sample ID use
            sampleids = [r['sampleid2'] for r in recs]
        except KeyError:
            print >> sys.stderr, 'not all samples have ID:'
            for d in recs:
                print >> sys.stderr, d.get('sampleid','MISSING'),d
            raise
    wells = [r['adapter'] for r in recs]

    if len(set(sampleids)) != len(sampleids):
        raise ValueError, '%s sampleids, %s unique' % (len(sampleids),len(set(sampleids)))
    if len(set(wells)) != len(wells):
        raise ValueError, '%s wells, %s unique' % (len(wells),len(set(wells)))

    indiv_data = {}
    for r in recs:
        indiv_data[idxlookup[r['adaptersversion']][r['adapter']]] = r

    return indiv_data

def match_index(t,idx_d,idx_len=None,mismatch_allowed=1):
    '''given an index read sequence a dictionary of form {"<read_sequence>":"<index_number>" ...}
    returns <index_number> if the best match is the only index within mismatch_allowed

    '''

    # removed in variable length setup
    #if idx_len is None:
    #    idx_len = list(set([len(k) for k in idx_d]))[0]
	
    tagdist = sorted([(distance(t_this,t[:len(t_this)]),t_this) for t_this in idx_d.keys()])
    if tagdist[0][0] <= mismatch_allowed and tagdist[1][0] > mismatch_allowed:
        return idx_d[tagdist[0][1]]
    else:
        return None

def sam_line_to_fastq(samline,idx_field=None,idx_d=None,idx_len=None):
    '''converts a sam line to 4-line fastq string.
    If idx_field is present, matches strings in this field with match_index using idx_d (required) and idx_len (which can be none)
    If idx_field is present, return is (string,idx) else return is string
    
    TO DO:
    CURRENTLY ASSUMES READ 1 (should figure this out from flag)
    '''

    qname,flag,rname,pos,mapq,cigar,mrnm,mpos,tlen,seq,qual,opt = samline.split(None,11)

    opts = dict([(s.split(':')[0],s.split(':')[-1]) for s in opt.split()])

    if idx_field is not None and idx_d is not None and idx_field in opts:
        if idx_len is None:
            idx_len = len(idx_field)
        fqstr = '@%s#%s/1\n%s\n+\n%s\n' % (qname,opts[idx_field][:idx_len],seq,qual)
        return (fqstr,match_index(opts[idx_field][:idx_len],idx_d,idx_len))
    else:
        fqstr = '@%s#0/1\n%s\n+\n%s\n' % (qname,seq,qual)
        return fqstr
    

def assign_read_to_indiv(line,indiv_data,mismatch_allowed=1, \
		indiv_reads_out_pattern=None,fhdict=None,passfh=None,read2_has_idx=None, \
		trim_Q2=False,min_readlen=None,lnum=4,output_lnum=4,baseQ_in=None,baseQ_out=None):
    '''given a fastq line (actually a list of [read_name,seq,qual_str]), and an indiv_data object (see get_individual_data_for_lane)

    assigns the read to an individual based on the index tag, strips the index sequence and quality positions,
    converts quality to list of integers, and returns the sampleid, sequence and quality
    
    if a pattern is specified for output (like "/path/to/per-indiv-data/%s_s_1_1_sequence.txt")
    will also generate per-individual fastqs.
    
    using a single fhdict and passfh is highly recommended (i.e. creating beforehand and passing as arguments),
    but will be generated if absent.

	FUTURE PLANS:
    if min_readlen is set, will "pass" reads shorter than min_readlen
    if trim_Q2 is True will remove all terminal quality 2 bases.
    If this reduces a read to less then min_readlen good bases, sends to pass
    
    
    returns indiv,read,qual
    
    Paired-Ends (PE) HANDLING:
    if line and indiv_reads_out_pattern are 2-tuples, treats reads as paired-end.
    This requires that read2_has_idx be either True or False
    if False, both reads handled per the index bases of line[0]
    if True, both reads assesssed for index bases, if they DO NOT DISAGREE both reads handled per consensus

    fhdict keys for PE (line is 2-tuple) are 2-tuples (<indiv>,<readnum>) i.e. (BW001,1)

    if passfh supplied, must also be 2-tuple

    returns indiv, (read1, read2), (q1, q2)
    '''

    #idxlen = len(indiv_data.keys()[0])

    if isinstance(line, tuple) and len(line) == 2:
        if (isinstance(indiv_reads_out_pattern, tuple) and len(indiv_reads_out_pattern) == 2) or indiv_reads_out_pattern is None:
            if read2_has_idx is not None:
                if indiv_reads_out_pattern is not None:
                    if fhdict is None:
                        fhdict = {}
                    if passfh is None:
                        passfh = [smartopen(p % 'pass','w') for p in indiv_reads_out_pattern]

                indiv = None
                heads = [l[0] for l in line]
                ss = [l[1] for l in line]
                qstrs = [l[2] for l in line]
                
                if baseQ_in is None:
                	bqs = list(set([get_baseQ(qs) for qs in qstrs if get_baseQ(qs) is not None]))
                	if len(bqs) == 1:
                		baseQ_in = bqs[0]
                	else:
                		raise ValueError,'bqs: %s' % bqs
                if baseQ_out is None:
                	baseQ_out = baseQ_in

                if len(set([h.split()[0][:-1] for h in heads])) != 1:
                    raise ValueError, 'read headers not identical prior to last character; %s' % heads 

                if read2_has_idx: #check that indices are concordant
                    #ts = [s[:idxlen] for s in ss]
                    #tqs = [qstr[:idxlen] for qstr in qstrs]
                    tagdists = [sorted([(distance(t_this,s[:len(t_this)]),t_this) for t_this in indiv_data.keys()]) for s in ss]
                    try:
                        indiv_cand = [indiv_data[tagdist[0][1]]['sampleid'] for tagdist in tagdists \
                                      if tagdist[0][0] <= mismatch_allowed and tagdist[1][0] > mismatch_allowed]
                    except:
                        indiv_cand = [indiv_data[tagdist[0][1]]['sampleid2'] for tagdist in tagdists \
                                      if tagdist[0][0] <= mismatch_allowed and tagdist[1][0] > mismatch_allowed]
                    if len(set(indiv_cand)) == 1:
                        idxlen = len(tagdist[0][1])
                        ts = [s[:idxlen] for s in ss]
                        tqs = [qstr[:idxlen] for qstr in qstrs]
                        indiv = indiv_cand[0]
                        read = [s[idxlen:] for s in ss]
                        qual = [[ord(c)-baseQ_in for c in qstr[idxlen:]] for qstr in qstrs]
                    
                else: #dump both reads per the first
                    #t = ss[0][:idxlen] #tag from read1
                    #ts = [t]*2 # hack for getting tag into both reads, below
                    #tqs = [qstrs[0][:idxlen]]*2
                    tagdist = sorted([(distance(t_this,ss[0][:len(t_this)]),t_this) for t_this in indiv_data.keys()])
                    if tagdist[0][0] <= mismatch_allowed and tagdist[1][0] > mismatch_allowed:
                        indiv = indiv_data[tagdist[0][1]]['sampleid']
                        idxlen = len(tagdist[0][1])
                        ts = [t]*2
                        tqs = [qstrs[0][:idxlen]]*2
                        read = [ss[0][idxlen:],ss[1]]
                        qual = [[ord(c)-baseQ_in for c in qstrs[0][idxlen:]],[ord(c)-baseQ_in for c in qstrs[1]]]

                if indiv is None:
                    read = ss
                    qual = [[ord(c)-baseQ_in for c in qstr] for qstr in qstrs]
                    if passfh is not None:
                        for id,s,q,fh in zip(heads,read,qual,passfh):
                            fh.write(as_fq_line(id,s,q,baseQ_out,output_lnum,))
                else:
                    if indiv_reads_out_pattern is not None:
                        for h,t,tq,s,q,rn,pat in zip(heads,ts,tqs,read,qual,[1,2],indiv_reads_out_pattern):
                            newhead = '%s %s:%s' % (h,t,tq)
                            try:
                                fhdict[(indiv,rn)].write(as_fq_line(newhead,s,q,baseQ_out,output_lnum))
                            except KeyError:
                                fhdict[(indiv,rn)] = smartopen(pat % indiv,'w')
                                fhdict[(indiv,rn)].write(as_fq_line(newhead,s,q,baseQ_out,output_lnum))
                                
                qual = [numpy.array(q,dtype=int) for q in qual]
                
            else:
                raise ValueError, 'read2_has_idx cannot be None for PE reads'
        else:
            raise ValueError, 'PE handling invoked, but indiv_out_pattern does not match; must be 2-tuple or None, is: %s' % indiv_reads_out_pattern
    else:

        if indiv_reads_out_pattern is not None:
            if fhdict is None:
                fhdict = {}
            if passfh is None:
                passfh = smartopen(indiv_reads_out_pattern % 'pass','w')

        head,s,qstr = line

        if baseQ_in is None:
            if get_baseQ(qstr) is None:
                raise ValueError,'could not determine qual base (33 or 64): %s' % qstr
            else:
                baseQ_in = get_baseQ(qstr)
        if baseQ_out is None:
            baseQ_out = baseQ_in


        #t = s[:idxlen]
    
        tagdist = sorted([(distance(t_this,s[:len(t_this)]),t_this) for t_this in indiv_data.keys()])
        if tagdist[0][0] <= mismatch_allowed and tagdist[1][0] > mismatch_allowed:
            indiv = indiv_data[tagdist[0][1]]['sampleid']
            idxlen = len(tagdist[0][1])
            t = s[:idxlen]
            read = s[idxlen:]
            qual = [ord(c)-baseQ_in for c in qstr[idxlen:]]
            if indiv_reads_out_pattern is not None:
                newhead = '%s:%s:%s' % (head,t,qstr[:idxlen])
                try:
                    fhdict[indiv].write(as_fq_line(newhead,read,qual,baseQ_out,output_lnum))
                except KeyError:
                    fhdict[indiv] = smartopen(indiv_reads_out_pattern % indiv,'w')
                    fhdict[indiv].write(as_fq_line(newhead,read,qual,baseQ_out,output_lnum))
        else:
            indiv = None
            read = s
            qual = [ord(c)-baseQ_in for c in qstr]
            if passfh is not None:
                passfh.write(as_fq_line(head,s,qual,baseQ_out,output_lnum))

        qual = numpy.array(qual,dtype=int)
    return indiv,read,qual

def next_read_from_fh(fh,lnum=None):

    if lnum is None:
        if smartopen(fh.name).read(1) == '@':
	    lnum = 4
	else:
	    lnum = 1

    if lnum == 1:
        return fh.readline().strip().rsplit(':',2)
    elif lnum == 4:
        rl = [fh.readline().strip() for i in range(lnum)]
        return [ rl[0][1:], rl[1], rl[3] ]

def as_fq4_lines(id,s,q,baseQ=None):
    if baseQ is None:
        return '\n'.join(['@'+id] + [s,'+',q+'\n'])
    else:
        return '\n'.join(['@'+id] + [s,'+',(''.join([chr(n+baseQ) for n in q]))+'\n'])

def as_fq1_line(id,s,q,baseQ):
    return ':'.join([id] + [s,(''.join([chr(n+baseQ) for n in q]))+'\n'])

def as_fq_line(id,s,q,baseQ,lnum):
    if lnum == 4:
        return as_fq4_lines(id,s,q,baseQ)
    elif lnum == 1:
        return as_fq1_line(id,s,q,baseQ)

def store_SR(all_quality,indiv,read,qual):
    store_read1(all_quality,indiv,read,qual)

def store_PE(all_quality,indiv,read,qual):
    store_read1(all_quality,indiv,read[0],qual[0])
    store_read2(all_quality,indiv,read[0],read[1])

def store_read1(all_quality,indiv,read,qual):
    try:
        all_quality[read]['count'][indiv] += 1
        all_quality[read]['sum_quality'] += qual
    except KeyError:
        all_quality[read]['count'] = defaultdict(int)
        all_quality[read]['count'][indiv] += 1
        all_quality[read]['sum_quality'] = qual 


def store_read2(all_quality,indiv,read1,read2):
    try:
        all_quality[read1]['read2'][read2] += 1
    except KeyError:
        all_quality[read1]['read2'] = defaultdict(int)
        all_quality[read1]['read2'][read2] += 1


def write_uniqued(all_quality,outfile,baseQ):
    ofh = smartopen(outfile,'w')

    for seq in all_quality.keys():
        aqd = all_quality[seq]
        ind,indcount = dezip(sorted([(k,v) for k,v in aqd['count'].items()],reverse=True,key = lambda x:x[1]))
        c = sum(indcount)
        q = ''.join([chr(i+baseQ) for i in aqd['sum_quality'] / c])
        if aqd.has_key('read2') and any([v > 1 for v in aqd['read2'].values()]):
            r2,r2count = dezip(sorted([(k,v) for k,v in aqd['read2'].items() if v > 1],reverse=True,key = lambda x:x[1]))
        else:
            r2,r2count = '.','.'
        
        line =  '%s\t%s\t%s\t%s\t%s\t%s\t%s\n' % (seq,c,q, ','.join(ind), ','.join([str(i) for i in indcount]), ','.join(r2), ','.join([str(i) for i in r2count]))
        ofh.write(line)

    ofh.close()


if __name__ == '__main__':

    import argparse

    ds =  ' [%(default)s]'
    #create command line parser
    parser = argparse.ArgumentParser(description='performs index parsing and unique sequence tabulation')

    parser.add_argument('-w','--write_reads_by_indiv',action='store_true',help='enables creation of new .fastq files, one for each individual in reads_by_individual folder'+ds)
    parser.add_argument('-u','--no_uniqued',action='store_true',help='disables creation of .uniqued file'+ds)
    parser.add_argument('-r2idx','--read2_has_idx',action='store_true',help='if specified, individual index (barcode) is also present in read 2'+ds)
    parser.add_argument('-mc','--set_mincycles',default=0,type=int,help='truncate reads to this length (if not 0)'+ds)
    parser.add_argument('-s','--cutsite',default='AATTC',help='sequence left behind by restriction enzyme at read1 end of library NOT NECESSARILY FULL R.E. SITE'+ds)
    parser.add_argument('-iq','--base_Q_in',default=None,type=int,help='integer offset for quality scores IN INPUT.  Usually 33 for "sanger" style (newer illumina runs, input for BWA) or 64 for illumina/solexa (older illumina). If None, ascertain from data'+ds)
    parser.add_argument('-oq','--base_Q_out',default=33,type=int,help='integer offset for quality scores IN OUTPUT.  Usually 33 for "sanger" style (newer illumina runs, input for BWA) or 64 for illumina/solexa (older illumina). value None will output according to input'+ds)
    parser.add_argument('-ol','--output_lnum',default='4',choices=['1','4'],type=int,help='number of lines per record in fastq output if -w is specified (either older 1-line, or newer 4-line)'+ds)
    parser.add_argument('-fc','--flowcell',default=None,type=str,help='flowcell name (if None, derive from sequence infile path)'+ds)
    parser.add_argument('-l','--lane',default=None,type=str,help='lane (if None, derive from sequence infile name)'+ds)
    parser.add_argument('-idx','--index',default=None,type=str,help='multiplex index (if None, derive from sequence infile name)'+ds)

    parser.add_argument('-suf','--suffix',default=None,type=str,help='suffix for .uniqued file (permits processing split files)'+ds)

    parser.add_argument('-e','--estimate_error',action='store_true',help='invokes clustering to estimate error rate after completion of preprocessing'+ds)
    parser.add_argument('-ee','--est_err_engine',default='local',choices=['local','parallel','lsf'],help='use this engine for parallelizing error estimate (rtd_run -pe argument)'+ds)
    parser.add_argument('-ec','--est_err_cores',default=1,type=int,help='parallelize error estimate run over this number of cores (serial if less than 2) REQUIRES GNU PARALLEL'+ds)
    parser.add_argument('-ep','--est_err_parts',default=4,type=int,help='number of query files to split error estimate simliarity calculation into (see rtd_run -np argument)'+ds)
    parser.add_argument('-et','--est_err_threads',default=4,type=int,help='number of MCL expansion threads in clustering (see rtd_run -te argument)'+ds)
    parser.add_argument('-er','--est_err_radius',default=2,type=int,help='MCL radius argument (-I) for error estimate clustering'+ds)

    parser.add_argument('infiles',nargs='+',help='either 1 or 2 fastq files corresponding to reads from a single lane, and optionally read 2 sequences for that lane')

    opts = parser.parse_args()

    write_reads_by_indiv = opts.write_reads_by_indiv

    set_mincycles = opts.set_mincycles
    read2_has_idx = opts.read2_has_idx
    nticks = 20
    cutsite = opts.cutsite
    baseQ_in = opts.base_Q_in
    baseQ_out = opts.base_Q_out

    if len(opts.infiles) == 1:
        r1 = opts.infiles[0]
        fh = smartopen(r1)
        chr1 = fh.read(1)
        fh.seek(0)
    elif len(opts.infiles) == 2:
        r1,r2 = opts.infiles[0:2]
        fh = (smartopen(r1),smartopen(r2))
        chr1 = fh[0].read(1)
        fh[0].seek(0)
    else:
        raise ValueError, 'either one or two input fastq files must be specified, got %s' % opts.infiles
    # here forward, if isinstance(fh,tuple) then we're PE

    if chr1 == '@':
        lnum = 4
        print >> sys.stderr, 'using 4-line fastq'
    else:
        lnum = 1
        print >> sys.stderr, 'using 1-line fastq'

    qfh = smartopen(r1)
    while baseQ_in is None:
        baseQ_in = get_baseQ(next_read_from_fh(qfh,lnum)[2])
    qfh.close()
    
    if baseQ_out is None:
        baseQ_out = baseQ_in
	
    print >> sys.stderr, 'read qualities base %s, write qualities base %s' % (baseQ_in, baseQ_out)

    idxlookup = get_adapter_index_lookup()
    indivs = []

    if opts.flowcell is None:        
        fc = os.path.basename(os.path.dirname(r1))
    else:
        fc = opts.flowcell

    if opts.lane is None:
        lane = os.path.basename(r1)[2]
    else:
        lane = opts.lane

    nreads = get_read_count(r1,lnum)

    #index info append
    if opts.index is not None:
        index = opts.index
        idxstr = '_index%s' % index
    elif 'index' in os.path.basename(r1):
        index = os.path.basename(r1).split('index')[-1].split('.')[0]
        idxstr = '_index%s' % index
    else:
        index = None
        idxstr = ''

    if opts.suffix is not None:
        idxstr = idxstr+'_'+opts.suffix
    
    if isinstance(fh,tuple):
        nreads2 = get_read_count(r2,lnum)
        if nreads != nreads2:
            raise ValueError, '%s read count: %s; %s read count: %s' % (r1, nreads, r2, nreads2)

    indiv_data = get_individual_data_for_lane(idxlookup=idxlookup,fc=fc,lane=lane,index=index)

    adaptersversions = set([r['adaptersversion'] for r in indiv_data.values()])
    idxs = reduce(lambda x,y: x+y, [idxlookup[adver].values() for adver in adaptersversions])    
    idxlen = set(map(len,indiv_data.keys()))
    line = next_read_from_fh(smartopen(r1),lnum)
    readlen = len(line[-2]) #- idxlen
    print >> sys.stderr, '%s\n\t%s bp reads, %s / %s %s bp idxs used' % (r1,readlen,len(indiv_data),len(idxs),idxlen)


    if write_reads_by_indiv:
	outroot = os.path.dirname(r1)
        indiv_reads_out_base = os.path.join(outroot,'reads_by_individual/%s_lane%s%s/' % (fc,lane,idxstr))
        try:
            os.makedirs(indiv_reads_out_base)
	    os.system('chmod g+w '+indiv_reads_out_base.rstrip('/'))
	    print >> sys.stderr, indiv_reads_out_base,'created for individual output'
        except OSError:
            print >> sys.stderr, indiv_reads_out_base,'exists, using'
            
    else:
        indiv_reads_out_base = None

    #prep filehandles for faster processing, see assign_reads_to_indiv docstring
    if indiv_reads_out_base is not None:
        fhdict = {}	    
        if isinstance(fh,tuple):
            indiv_reads_out_pattern = tuple([os.path.join(indiv_reads_out_base,'%s_'+os.path.basename(r1)),os.path.join(indiv_reads_out_base,'%s_'+os.path.basename(r2))])
	    passfh = tuple([smartopen(indiv_reads_out_pattern[0] % 'pass', 'w'),smartopen(indiv_reads_out_pattern[1] % 'pass', 'w')])
	else:
            indiv_reads_out_pattern = os.path.join(indiv_reads_out_base,'%s_'+os.path.basename(r1))
            passfh = smartopen(indiv_reads_out_pattern % 'pass', 'w')
    else:
        indiv_reads_out_pattern = None
        fhdict = None
        passfh = None

    all_quality = defaultdict(dict)
    tickon = nreads/nticks
    if tickon < 1:
    	tickon = 1
    print >> sys.stderr, '\tloading'

    if isinstance(fh,tuple): #PE
        outfile = os.path.join(os.path.dirname(r1), '%s_lane%s_PE%s.uniqued.gz' % (fc,lane,idxstr))
        for i in xrange(nreads):
            if i%tickon==0: print >> sys.stderr, '\t\t%s / %s' % (i,nreads)

            l = tuple([next_read_from_fh(h,lnum) for h in fh])
            indiv,read,qual = assign_read_to_indiv(l,indiv_data,indiv_reads_out_pattern=indiv_reads_out_pattern,\
            										fhdict=fhdict,passfh=passfh,read2_has_idx=read2_has_idx,\
            										baseQ_in=baseQ_in,baseQ_out=baseQ_out,lnum=lnum,output_lnum=opts.output_lnum)
            if indiv is not None and not opts.no_uniqued:
                #store PE
                store_PE(all_quality,indiv,read,qual)
    else: #SR
        outfile = os.path.join(os.path.dirname(r1), '%s_lane%s_SR%s.uniqued.gz' % (fc,lane,idxstr))
        for i in xrange(nreads):
            if i%tickon==0: print >> sys.stderr, '\t\t%s / %s' % (i,nreads)

            l = next_read_from_fh(fh,lnum)
            
            indiv,read,qual = assign_read_to_indiv(l,indiv_data,indiv_reads_out_pattern=indiv_reads_out_pattern,\
            										fhdict=fhdict,passfh=passfh,baseQ_in=baseQ_in,baseQ_out=baseQ_out,lnum=lnum,output_lnum=opts.output_lnum)
            if indiv is not None and not opts.no_uniqued:
                #store SR
                store_SR(all_quality,indiv,read,qual)
                

    if fhdict is not None:
        for v in fhdict.values():
            v.close()
    if passfh is not None:
        if isinstance(passfh,tuple):
            for pfh in passfh:
	        pfh.close()
	else:
            passfh.close()


    if opts.no_uniqued:
        print >> sys.stderr, '.uniqued output disabled, skip postprocessing on .uniqued'
    else:
        write_uniqued(all_quality,outfile,baseQ_out)
        print >> sys.stderr, 'output written to',outfile

        # summarize stats buggy; pool_lookup throws errors and call to Util isn't portable
        # disable until fixed
        #print >> sys.stderr, 'generate preprocess summary (summarize_sequencing_stats.py)'
        #ret = os.system(os.path.join(RTDROOT,'summarize_sequencing_stats.py %s > %s.stats' % (outfile,outfile)))

        if opts.estimate_error:
            os.system(os.path.join(RTDROOT,'estimate_error_by_clustering.py %s %s %s %s %s %s %s' % (outfile, opts.cutsite, opts.est_err_engine, opts.est_err_cores, opts.est_err_parts, opts.est_err_threads, opts.est_err_radius)))
    
    #done